DETAILED ACTION
This is a non-final Office Action on the merits in response to communications filed by Applicant on April 27th, 2026. Claims 1-20 are currently pending and examined below.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendments to the claims, filed on April 27th, 2026, have been entered. Claims 1, 8, and 15 are currently amended and pending, claim 9 is as previously presented, and claims 2-7, 10-14, and 16-20 are original, unamended, and pending.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 3-4, 8, 10-11, 15, and 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 11100339 B2 ("Ma") in view of US 2023/0399021 A1 ("Zhang").
Regarding claim 1, Ma teaches a path determination system comprising a processor and a memory, the processor configured to (Ma: Figure 5 computing device 504, Column 13 lines 53-67, “FIG. 5 illustrates a block diagram of an example system that implements the techniques discussed herein. In some instances, the system 500 may include a vehicle 502, which may represent the autonomous vehicle 102 in FIG. 1.”, Column 15 lines 23-50, “Additionally, the drive component(s) 512 may include a drive component controller which may receive and preprocess data from the sensor(s) and to control operation of the various vehicle systems. In some instances, the drive component controller may include one or more processors and memory communicatively coupled with the one or more processors. The memory may store one or more components to perform various functionalities of the drive component(s) 512.”, Column 15 lines 51-65, “The vehicle computing device(s) 504 may include processor(s) 518 and memory 520 communicatively coupled with the one or more processors 518. Computing device(s) 514 may also include processor(s) 522, and/or memory 524. The processor(s) 518 and/or 522 may be any suitable processor capable of executing instructions to process data and perform operations as described herein.”):
receive sensor data from one or more sensors of an autonomous vehicle (Ma: Column 4 lines 39-52, “According to the techniques discussed herein, the autonomous vehicle 102 may receive sensor data from sensor(s) 104 of the autonomous vehicle 102. For example, the sensor(s) 104 may include a location sensor (e.g., a global positioning system (GPS) sensor), an inertia sensor (e.g., an accelerometer sensor, a gyroscope sensor, etc.), a magnetic field sensor (e.g., a compass), a position/velocity/acceleration sensor (e.g., a speedometer, a drive system sensor), a depth position sensor ( e.g., a lidar sensor, a radar sensor, a sonar sensor, a time of flight (ToF) camera, a depth camera), an image sensor (e.g., a visible light spectrum camera, a depth camera, an infrared camera), an audio sensor (e.g., a microphone), and/or environmental sensor (e.g., a barometer, a hygrometer, etc.).”);
identify, based on the sensor data, (i) one or more forward travel lanes and (ii) one or more objects (Ma: Column 5 lines 39-48, “The perception engine 114 may receive sensor data from sensor( s) 104 and may determine perception data therefrom. For example, perception engine 114 may include one or more machine-learned (ML) models and/or other computer executable instructions for detecting, identifying, segmenting, classifying, and/or tracking objects from sensor data collected from the environment of the autonomous vehicle 102. In some examples, the perception engine 114 may comprise a component for detecting whether a lane is open or closed.”, Column 5 lines 49-62, “In the illustrated example scenario 100, autonomous vehicle 102 may receive sensor data from one or more of the sensor(s) 104 as the autonomous vehicle 102 approaches a collection of traffic cones 120. Traffic cones 120 may be one example of safety objects associated with a lane closure. The perception engine 114 may comprise one or more ML models for detecting, based at least in part on the sensor data, object(s) in the environment surrounding the autonomous vehicle 102 and/or classifying the object(s). For example, the autonomous vehicle 102 may receive an image and/or point cloud data (e.g., data from lidar, radar, sonar), which the autonomous vehicle 102 may determine is associated with one or more safety objects (e.g., by determining an object detection is associated with a safety class).”, Column 6 lines 9-23, “In some examples, if at least one of the object detections generated by the perception engine 114 indicates a classification associated with a safety object (i.e., a "safety class"), the perception engine 114 may trigger a lane closure analysis. In an additional or alternate example, the autonomous vehicle 102 may analyze at least a current lane 122; any adjacent lane(s), such as adjacent lane 124; and/or any other lane to determine whether the lane is open or closed. For example, the perception engine 114 may continuously or periodically conduct the lane closure analysis described herein, regardless of whether a safety object has been detected, and/or if a safety object is detected the perception engine 114 may trigger a lane analysis in addition to a periodic lane analysis.”. As can be seen from the cited passages, the system is clearly configured to detect one or more objects based the sensor data. Additionally, as can be seen in Column 6 lines 9-23, the perception engine is configured to identify the travel lanes of the road the vehicle is traveling.);
determine the one or more objects form a barrier indicating at least partial closure of the one or more forward travel lanes (Ma: Column 9 lines 9-24, “At operation 220, example process 200 may comprise determining whether a distance between a dilated object detection another object detection, another dilated object detection, and/or an extent of a lane and/or roadway meets or exceeds a distance threshold, according to any of the techniques discussed herein. In some examples, the distance threshold may correspond to a width and/or length of the autonomous vehicle ( e.g., depending on the dimension in which the distance was measured-in the depicted example, the distance threshold may be based at least in part on a width of the autonomous vehicle) and/or a tolerance. Operation 220 may functionally determine whether the autonomous vehicle would fit between dilated object detections (along a longitudinal or lateral axis of the vehicle, for example). If the distance is less than the distance threshold, example process 200 may continue to operation 222.”, Column 9 lines 25-44, “At operation 222, example process 200 may comprise determining a closed status indicating that the lane is closed, according to any of the techniques discussed herein. In some examples, operation 222 may comprise any method of setting and/or saving a state in association with the analyzed lane such as, for example, flipping a flag in a register, transitioning a state machine to a state to identify the lane as being closed. For example, FIG. 2A depicts lane states 224, which comprise identifying an analyzed lane as being closed. In some instances, the status may be associated with a portion of a lane based at least in part on a dilated object detection and/or object detection closest to the autonomous vehicle.”. The cited passages teach determining if the detected objects are spaced in such a way that the vehicle can fit between them and if the space between the objects ca not allow the vehicle to pass, setting the lane to a closed status. This is clearly a method of determining if the objects create a blockage or barrier on the road.);
determine a first boundary and a second boundary that together define an alternative travel path deviating at least in part from the one or more forward travel lanes (Ma: Figures 3 and 4, Column 11 line 60 – Column 12 line 3, “FIG. 3 illustrates an aerial view of an example scenario 300 in which a group of safety objects designate a new lane that is not associated with traditional lane markings. The example scenario 300 in FIG. 3 depicts a shifted taper, although many other lane modifications exist such as, for example, a flagging taper, shoulder taper, merging taper, one-lane two-way traffic taper, and the like. In some examples, the techniques described herein may determine that all available lanes ( e.g., lanes associated with a same direction of travel as the vehicle) are closed in a scenario like example scenario 300.”, Column 12 lines 4-36, “Example scenario 300 includes a roadway having two directions of traffic, direction of traffic 302 and direction of traffic 304, where each direction of traffic has two lanes associated therewith. Direction of traffic 304 includes two traditional lanes (left traditional lane 306 and right traditional lane 308), as demarcated according to the hashed lane markers and bounded by the double (yellow) line 310 and a roadway extent 312. For the sake of example, it is assumed that the autonomous vehicle 102 has already determined that the right traditional lane 308 is closed (e.g., the autonomous vehicle may have stored and/or maintained a closed status in association with the right traditional lane 308) based at least in part on a collection of safety objects in the roadway (e.g., unlabeled due to their number and represented as circles). However, the autonomous vehicle 102, upon coming upon the shifted taper, may determine that the left traditional lane 306 is closed (e.g., due to the safety objects in that lane) and/or that a lane 314 associated with the (opposite) direction of traffic 302 is unavailable since the direction of traffic 302 is opposite direction of traffic 304 with which the autonomous vehicle 102 is associated and/or because of detecting the double line 314. In other words, by applying the techniques discussed in regard to FIG. 2, the autonomous vehicle 102 may determine that all the (traditional) lane(s) associated with a direction of traffic are blocked (e.g., no traditional lane has an opening wide enough for an autonomous vehicle 102 to pass through according to the techniques discussed above in regard to FIG. 2). The autonomous vehicle 102 may be equipped with additional or alternative techniques for determining an alternative lane shape and/or an open/closed status associated therewith. The alternative lane shape may eschew traditional lane markings in some instances.” Column 12 line 63 – Column 13 line 4, “The autonomous vehicle 102 may generate such a shape to have a contiguous boundary and such that there is at least one lane having a minimum width. The minimum width may be a distance that is greater than the threshold distance (so that the shape generated may avoid including safety objects of an opposite side of the tapered lane). In some examples, an ML model may be trained to determine such a shape ( e.g., based at least in part on a classification task, clustering task, and/or the like).” Column 13 lines 5-16, “In some examples, the autonomous vehicle 102 may repeat this for a second set of safety objects (e.g., the leftward safety objects) associated with a second closed lane status and/or based at least in part on an open lane status. In other words, the autonomous vehicle 102 may inversely determine a shape that positively identifies an open lane, based at least in part on an open lane status and one or more safety objects. In some examples, the autonomous vehicle may detect a safety sign that symbolizes a taper, lane merge, and/or other traffic modification and may determine the alternative lane shape based at least in part on the symbols on the safety sign.”, Column 13 lines 18-51, “FIG. 4 illustrates three alternative lane shapes, a first alternative lane shape 404 associated with a closed lane status, a second alternative lane shape 406 associated with an open lane status, and a third alternative lane shape 408 associated with a closed lane status.”. The cited passages clearly shows that the system determines a first boundary and a second boundary that define an alternate travel path.);
control the autonomous vehicle to travel the alternative travel path (Ma: Column 7 lines 1 -32, “In the depicted example, planner may generate and/or select trajectory 136 based at least in part on a closed lane status 134 associated with the current lane 122. Trajectory 136 may comprise instructions for actuating a drive system to cause the autonomous vehicle 102 to merge into an adjacent lane 124 (which may be associated with a same direction of travel and an open lane status 132).”).
Ma does not teach in response to the barrier indicating at least partial closure of the one or more forward travel lanes, filter a variability in position of the one or more objects forming the barrier to determine a first boundary and a second boundary that together define an alternative travel path deviating at least in part from the one or more forward travel lanes;
determine that a minimum width between respective points on the first boundary and the second boundary is at least a width sized for forward travel of the autonomous vehicle; and
in response to a minimum width being at least sized for forward travel of the autonomous vehicle, control the autonomous vehicle to travel the alternative travel path.
Zhang, in the same endeavor, teaches in response to the barrier indicating at least partial closure of the one or more forward travel lanes, filter a variability in position of the one or more objects forming the barrier to determine a first boundary and a second boundary that together define an alternative travel path deviating at least in part from the one or more forward travel lanes (Zhang: ¶ 0052, “At block 404, the processor may detect, based on the output from the perception system, one or more objects indicating a presence of a restricted traffic zone. For example, the processor may be configured to detect objects that are used by workers to signal the presence of a restricted traffic zone and/or objects that are typically found in restricted traffic zones. Examples of such objects include cones, roadblocks, barricades, barriers, barrels, emergency lights, emergency signs, emergency vehicles, other types of vehicles, pedestrians, first responders, and construction zone signs.”, ¶ 0060, “At block 508, the processor may generate a drivable area through the restricted traffic zone based on at least one of the prior map 502, the obstruction map 504, and the detected one or more objects. As part of block 508, the processor may also determine whether the autonomous vehicle 105 is entering a restricted traffic zone based on the prior map 502, the obstruction map 504, and the perception output 506. In some implementations, the processor ma also determine the type of the restricted traffic zone (e.g., an emergency zone, a work zone, or a traffic control zone).”, ¶ 0061, “The processor may use one or more of the objects from the perception output 506 in generating the drivable area. For example, the processor may connect a plurality of the objects to form a curve and use the curve as a boundary between the drivable area and a non-drivable area. For example, when the objects include a plurality of cones, the cones may have been placed by a worker to delineate the boundary of the drivable area within the restricted traffic zone. Thus, by connecting the cones into a curve, the processor can generate the boundary between the drivable and non-drivable areas based on the information communicated by the presence of the cones. The processor may determine the boundary using a similar process for other objects that indicate the boundary of the drivable area. For example, the processor may form curves based on the location of roadblocks, barricades, barriers, and/or barrels.”, ¶ 0065, “In a third example, the processor may determine that the curve does not meet the criteria for either of the first and second examples discussed above. In this case, the processor may determine one or more new lanes based on the location of the curve. For example, the processor may identify a new lane substantially parallel to the curve. This new lane may not be fixed to the position of any lane within the prior map 502. The generation of the new lane map may be analogous to shifting the position of the prior lanes and/or "repainting" the lanes based on the location of the curve.”, ¶ 0078, “In certain embodiments, the processor can further be configured to apply a temporal cone filter to reduce false positives associated with detected cones. As described herein, the processor can be configured to determine the boundary of a restricted traffic zone (e.g., a lane closure) based on the detected locations of cones and/or posts. However, under certain circumstances, the processor may detect a cone or post that represents a false positive for the location of the boundary. For example, one or more cone(s) and/or post(s) may have been moved from their initial locations, and thus, the location(s) of the moved cone(s) and/or post(s) may be falsely interpreted as the boundary of the restricted traffic zone. By using the temporal cone filter, the processor can reduce the occurrence of false positives due to change(s) in the locations of cone(s) and/or post(s).”, ¶ 0079, “As part of implementing the temporal cone filter, the processor can divide the roadway into a grid. Depending on the embodiment, the grid may be the same as the grid of the occupancy grid map generated based on the perception output 506. However, in other embodiments, the grid used in the temporal cone filter may be independent of other grids generated by the processor. In some embodiments, the grid can include a coordinate system, such as an east-north-up (ENU) coordinate system, however, aspects of this disclosure are not limited thereto. In one example, the grid may be formed of 1 meter by 1 meter square grid positions.”, ¶ 0080, “The processor can assign each of the cones and/or posts detected in the perception output 506 to a position within the grid. The processor can determine whether the number of cones and/or posts within a given grid position is greater than a threshold number. In response to determining that the number of cones and/or posts within the given grid position is greater than the threshold number, the processor can determine that the grid position forms part of the boundary of the restricted traffic zone ( e.g., the lane closure). When the number of cones and/or posts within the given grid position is equal to or less than the threshold number, the processor can determine that the grid position does not form part of the boundary of the restricted traffic zone ( e.g., the lane closure), in other words, filtering any cones or posts detected at the given grid position. By filtering the cones and/or posts in this way, the processor can reduce the number of false positives described above.”. The cited passages clearly teaches a method of filtering out the variability in position of cones that are used to define the boundaries of the alternate travel lane.);
determine that a minimum width between respective points on the first boundary and the second boundary is at least a width sized for forward travel of the autonomous vehicle (Zhang: ¶ 0017, “In some embodiments, the method further comprises: determining that a minimum width of the drivable area is less than a predetermined width; and in response to determining that the minimum width is less than the predetermined width, determining that the autonomous vehicle should perform the minimum risk condition maneuver.”, ¶ 0052, “At block 404, the processor may detect, based on the output from the perception system, one or more objects indicating a presence of a restricted traffic zone. For example, the processor may be configured to detect objects that are used by workers to signal the presence of a restricted traffic zone and/or objects that are typically found in restricted traffic zones. Examples of such objects include cones, roadblocks, barricades, barriers, barrels, emergency lights, emergency signs, emergency vehicles, other types of vehicles, pedestrians, first responders, and construction zone signs.”, ¶ 0060, “At block 508, the processor may generate a drivable area through the restricted traffic zone based on at least one of the prior map 502, the obstruction map 504, and the detected one or more objects. As part of block 508, the processor may also determine whether the autonomous vehicle 105 is entering a restricted traffic zone based on the prior map 502, the obstruction map 504, and the perception output 506. In some implementations, the processor ma also determine the type of the restricted traffic zone (e.g., an emergency zone, a work zone, or a traffic control zone).”, ¶ 0061, “The processor may use one or more of the objects from the perception output 506 in generating the drivable area. For example, the processor may connect a plurality of the objects to form a curve and use the curve as a boundary between the drivable area and a non-drivable area. For example, when the objects include a plurality of cones, the cones may have been placed by a worker to delineate the boundary of the drivable area within the restricted traffic zone. Thus, by connecting the cones into a curve, the processor can generate the boundary between the drivable and non-drivable areas based on the information communicated by the presence of the cones. The processor may determine the boundary using a similar process for other objects that indicate the boundary of the drivable area. For example, the processor may form curves based on the location of roadblocks, barricades, barriers, and/or barrels.”, ¶ 0065, “In a third example, the processor may determine that the curve does not meet the criteria for either of the first and second examples discussed above. In this case, the processor may determine one or more new lanes based on the location of the curve. For example, the processor may identify a new lane substantially parallel to the curve. This new lane may not be fixed to the position of any lane within the prior map 502. The generation of the new lane map may be analogous to shifting the position of the prior lanes and/or "repainting" the lanes based on the location of the curve.”, ¶ 0068, “In some implementations, one or more of the requirements used for determining whether the autonomous vehicle 105 can drive through the restricted traffic zone may vary depending on the current conditions of the environment. For example, the requirements can include one or more of the following: current traffic conditions, a current speed of the autonomous vehicle 105, a current perception range of the perception system, and a minimum width of the drivable area.”, ¶ 0069, “For example, the processor can determine whether a minimum width of the drivable area is less than a predetermined width. In response to determining that the minimum width is less than the predetermined width, the processor may determine that the autonomous vehicle 105 should perform the MRC maneuver. In some implementations, the predetermined width may be 3 meters, however, this disclosure is not limited thereto and the predetermined width may be less than or greater than 3 meters. Depending on the embodiment, the predetermined width may be based on a width of the autonomous vehicle 105.”, ¶ 0070, “In response to determining that the autonomous vehicle 105 cannot drive through the restricted traffic zone, the processor may cause the autonomous vehicle 105 to perform the MRC maneuver. The processor may also determine whether the MRC maneuver should include the autonomous vehicle 105 stopping in a current lane, stopping in an emergency lane, or stopping on a different portion of the drivable area based on the drivable area.”. The cited passages clearly shows that the system is configured to determine if the minimum width of the drivable area defined by the detected objects is at or above a threshold width that indicates the area is suitable for the vehicle to travel through.); and
in response to a minimum width being at least sized for forward travel of the autonomous vehicle, control the autonomous vehicle to travel the alternative travel path (Zhang: ¶ 0017, “In some embodiments, the method further comprises: determining that a minimum width of the drivable area is less than a predetermined width; and in response to determining that the minimum width is less than the predetermined width, determining that the autonomous vehicle should perform the minimum risk condition maneuver.”, ¶ 0069, “For example, the processor can determine whether a minimum width of the drivable area is less than a predetermined width. In response to determining that the minimum width is less than the predetermined width, the processor may determine that the autonomous vehicle 105 should perform the MRC maneuver. In some implementations, the predetermined width may be 3 meters, however, this disclosure is not limited thereto and the predetermined width may be less than or greater than 3 meters. Depending on the embodiment, the predetermined width may be based on a width of the autonomous vehicle 105.”, ¶ 0070, “In response to determining that the autonomous vehicle 105 cannot drive through the restricted traffic zone, the processor may cause the autonomous vehicle 105 to perform the MRC maneuver. The processor may also determine whether the MRC maneuver should include the autonomous vehicle 105 stopping in a current lane, stopping in an emergency lane, or stopping on a different portion of the drivable area based on the drivable area.”. One of ordinary skill in the art would recognize that if the minimum width of the drivable area is at or above the threshold, the vehicle will travel through the drivable area.).
Ma teaches a path determination system comprising a processor and a memory, the processor configured to: receive sensor data from one or more sensors of an autonomous vehicle; identify, based on the sensor data, (i) one or more forward travel lanes and (ii) one or more objects; determine the one or more objects form a barrier indicating at least partial closure of the one or more forward travel lanes; determine a first boundary and a second boundary that together define an alternative travel path deviating at least in part from the one or more forward travel lanes; control the autonomous vehicle to travel the alternative travel path. Ma does not teach in response to the barrier indicating at least partial closure of the one or more forward travel lanes, filter a variability in position of the one or more objects forming the barrier to determine a first boundary and a second boundary that together define an alternative travel path deviating at least in part from the one or more forward travel lanes; determine that a minimum width between respective points on the first boundary and the second boundary is at least a width sized for forward travel of the autonomous vehicle; and in response to a minimum width being at least sized for forward travel of the autonomous vehicle, control the autonomous vehicle to travel the alternative travel path. Zhang teaches in response to the barrier indicating at least partial closure of the one or more forward travel lanes, filter a variability in position of the one or more objects forming the barrier to determine a first boundary and a second boundary that together define an alternative travel path deviating at least in part from the one or more forward travel lanes; determine that a minimum width between respective points on the first boundary and the second boundary is at least a width sized for forward travel of the autonomous vehicle; and in response to a minimum width being at least sized for forward travel of the autonomous vehicle, control the autonomous vehicle to travel the alternative travel path. A person of ordinary skill in the art would have had the technological capabilities required to have modified the system taught in Ma with in response to the barrier indicating at least partial closure of the one or more forward travel lanes, filter a variability in position of the one or more objects forming the barrier to determine a first boundary and a second boundary that together define an alternative travel path deviating at least in part from the one or more forward travel lanes; determine that a minimum width between respective points on the first boundary and the second boundary is at least a width sized for forward travel of the autonomous vehicle; and in response to a minimum width being at least sized for forward travel of the autonomous vehicle, control the autonomous vehicle to travel the alternative travel path taught in Zhang. Furthermore, the path determination system taught in Ma is already configured to determine if the space between detected objects along a vehicles path meet a width threshold such that the vehicle can pass through them and controls the vehicle to travel the alternat path, so modifying the system such that it determines if the alternated travel path meets a width threshold using the method taught in Zhang would not change or introduce new functionality. Additionally, the system taught in Ma is already configured to detect a plurality of objects that indicate a lane closure and use these object to define a boundary indicating a drivable area. As such, one of ordinary skill in the art would have been able to implement the filtering method taught in Zhang according to known methods. Ma is already configured to detect objects and define boundaries using said objects, so one of ordinary skill in the art would have been able to simply add the filtering method taught in Zhang. Such modifications would not have changed or introduced new functionality. No inventive effort would have been required. The combination would have yielded the predictable result of a path determination system comprising in response to the barrier indicating at least partial closure of the one or more forward travel lanes, filter a variability in position of the one or more objects forming the barrier to determine a first boundary and a second boundary that together define an alternative travel path deviating at least in part from the one or more forward travel lanes; determine that a minimum width between respective points on the first boundary and the second boundary is at least a width sized for forward travel of the autonomous vehicle; and in response to a minimum width being at least sized for forward travel of the autonomous vehicle, control the autonomous vehicle to travel the alternative travel path.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have combine the system taught in Ma with in response to the barrier indicating at least partial closure of the one or more forward travel lanes, filter a variability in position of the one or more objects forming the barrier to determine a first boundary and a second boundary that together define an alternative travel path deviating at least in part from the one or more forward travel lanes; determine that a minimum width between respective points on the first boundary and the second boundary is at least a width sized for forward travel of the autonomous vehicle; and in response to a minimum width being at least sized for forward travel of the autonomous vehicle, control the autonomous vehicle to travel the alternative travel path taught in Zhang with a reasonable expectation of success. One of ordinary skill in the art would have made this modification because the combination would have yielded predictable results.
Regarding claim 3, Ma in view of Zhang teaches wherein the processor is further configured to: while controlling the autonomous vehicle to travel the alternative travel path, identify an end of the closure of all of the one or more forward travel lanes (Ma: Column 10 lines 22-36, “Turning to FIG. 2D, at operation 232, example process 200 may comprise determining that sensor data indicates an absence of a safety object associated with a ( closed) lane for a duration of time that meets or exceeds a threshold duration, according to any of the techniques discussed herein. For example, example process 200 may comprise tracking a duration of time that no new object detections are received that identify an object and/or a safety object in a closed lane. That duration of time may be reset any time a new object detection identifies such an object (example process 200 may continue to operation 230 in such an instance). For example, the status associated with a closed lane may thereby be set to an open lane status shortly after an autonomous vehicle clears a last traffic cone, as depicted in FIG. 2D.”. The cited passage clearly shows that the method determines an end of the lane closure.);
control the autonomous vehicle to travel in one of the one or more forward travel lanes at the end of the closure (Ma: Column 7 lines 1-32, “The planner 112 may use the perception data, including the lane closed/open states discussed herein, to determine one or more trajectories to control the autonomous vehicle 102 to traverse a path or route and/or otherwise control operation of the autonomous vehicle 102, though any such operation may be performed in various other components. For example, the planner 112 may determine a route for the autonomous vehicle 102 from a first location to a second location; generate, substantially simultaneously, a plurality of potential trajectories for controlling motion of the autonomous vehicle 102 in accordance with a receding horizon technique ( e.g., 1 micro-second, half a second, every 10 seconds, and the like) and based at least in part on the lane states 130 (which may be associated with the map 116 and/or state tracker 118) to traverse the route ( e.g., in order to avoid any of the detected objects and/or to avoid operating in a closed lane); and select one of the potential trajectories as a trajectory 136 of the autonomous vehicle 102 that may be used to generate a drive control signal that may be transmitted to drive components of the autonomous vehicle 102.”. The system taught in Ma is clearly configured to control the vehicle to travel in the open travel lanes.).
Regarding claim 4, Ma in view of Zhang teaches wherein the processor is further configured to: determine the one or more forward travel lanes includes a plurality of forward travel lanes (Ma: Column 6 lines 9-23, “In some examples, if at least one of the object detections generated by the perception engine 114 indicates a classification associated with a safety object (i.e., a "safety class"), the perception engine 114 may trigger a lane closure analysis. In an additional or alternate example, the autonomous vehicle 102 may analyze at least a current lane 122; any adjacent lane(s), such as adjacent lane 124; and/or any other lane to determine whether the lane is open or closed. For example, the perception engine 114 may continuously or periodically conduct the lane closure analysis described herein, regardless of whether a safety object has been detected, and/or if a safety object is detected the perception engine 114 may trigger a lane analysis in addition to a periodic lane analysis.”. As can be seen in the cited passage, the perception engine is configured to identify the travel lanes of the road the vehicle is traveling.); and
when the barrier indicates closure of less than all of the plurality of forward travel lanes, control the autonomous vehicle to travel in an open travel lane of the plurality of forward travel lanes (Ma: Column 7 lines 1 -32, “In the depicted example, planner may generate and/or select trajectory 136 based at least in part on a closed lane status 134 associated with the current lane 122. Trajectory 136 may comprise instructions for actuating a drive system to cause the autonomous vehicle 102 to merge into an adjacent lane 124 (which may be associated with a same direction of travel and an open lane status 132).”. The cited passage clearly teaches controlling the vehicle to travel in an open travel lane when not all of the travel lanes are closed.).
Regarding claim 8, Ma teaches a method for path determination, the method comprising (Ma: Figures 2A-E, Column 7 lines 1-32, “In the depicted example, planner may generate and/or select trajectory 136 based at least in part on a closed lane status 134 associated with the current lane 122. Trajectory 136 may comprise instructions for actuating a drive system to cause the autonomous vehicle 102 to merge into an adjacent lane 124 (which may be associated with a same direction of travel and an open lane status 132).”, Column 7 lines 34-43, “FIGS. 2A-2E illustrate an example process 200 for analyzing a lane to determine a status of the lane. In some examples, example process 200 may be accomplished by component(s) of perception engine 114.”):
identifying, based on the sensor data, (i) one or more forward travel lanes and (ii) one or more objects (Ma: Column 5 lines 39-48, “The perception engine 114 may receive sensor data from sensor( s) 104 and may determine perception data therefrom. For example, perception engine 114 may include one or more machine-learned (ML) models and/or other computer executable instructions for detecting, identifying, segmenting, classifying, and/or tracking objects from sensor data collected from the environment of the autonomous vehicle 102. In some examples, the perception engine 114 may comprise a component for detecting whether a lane is open or closed.”, Column 5 lines 49-62, “In the illustrated example scenario 100, autonomous vehicle 102 may receive sensor data from one or more of the sensor(s) 104 as the autonomous vehicle 102 approaches a collection of traffic cones 120. Traffic cones 120 may be one example of safety objects associated with a lane closure. The perception engine 114 may comprise one or more ML models for detecting, based at least in part on the sensor data, object(s) in the environment surrounding the autonomous vehicle 102 and/or classifying the object(s). For example, the autonomous vehicle 102 may receive an image and/or point cloud data (e.g., data from lidar, radar, sonar), which the autonomous vehicle 102 may determine is associated with one or more safety objects (e.g., by determining an object detection is associated with a safety class).”, Column 6 lines 9-23, “In some examples, if at least one of the object detections generated by the perception engine 114 indicates a classification associated with a safety object (i.e., a "safety class"), the perception engine 114 may trigger a lane closure analysis. In an additional or alternate example, the autonomous vehicle 102 may analyze at least a current lane 122; any adjacent lane(s), such as adjacent lane 124; and/or any other lane to determine whether the lane is open or closed. For example, the perception engine 114 may continuously or periodically conduct the lane closure analysis described herein, regardless of whether a safety object has been detected, and/or if a safety object is detected the perception engine 114 may trigger a lane analysis in addition to a periodic lane analysis.”. As can be seen from the cited passages, the system is clearly configured to detect one or more objects based the sensor data. Additionally, as can be seen in Column 6 lines 9-23, the perception engine is configured to identify the travel lanes of the road the vehicle is traveling.);
determining the one or more objects form a barrier indicating ate least partial closure of the one or more forward travel lanes (Ma: Column 9 lines 9-24, “At operation 220, example process 200 may comprise determining whether a distance between a dilated object detection another object detection, another dilated object detection, and/or an extent of a lane and/or roadway meets or exceeds a distance threshold, according to any of the techniques discussed herein. In some examples, the distance threshold may correspond to a width and/or length of the autonomous vehicle ( e.g., depending on the dimension in which the distance was measured-in the depicted example, the distance threshold may be based at least in part on a width of the autonomous vehicle) and/or a tolerance. Operation 220 may functionally determine whether the autonomous vehicle would fit between dilated object detections (along a longitudinal or lateral axis of the vehicle, for example). If the distance is less than the distance threshold, example process 200 may continue to operation 222.”, Column 9 lines 25-44, “At operation 222, example process 200 may comprise determining a closed status indicating that the lane is closed, according to any of the techniques discussed herein. In some examples, operation 222 may comprise any method of setting and/or saving a state in association with the analyzed lane such as, for example, flipping a flag in a register, transitioning a state machine to a state to identify the lane as being closed. For example, FIG. 2A depicts lane states 224, which comprise identifying an analyzed lane as being closed. In some instances, the status may be associated with a portion of a lane based at least in part on a dilated object detection and/or object detection closest to the autonomous vehicle.”. The cited passages teach determining if the detected objects are spaced in such a way that the vehicle can fit between them and if the space between the objects ca not allow the vehicle to pass, setting the lane to a closed status. This is clearly a method of determining if the objects create a blockage or barrier on the road.);
determine a first boundary and a second boundary that together define an alternative travel path deviating at least in part from the one or more forward travel lanes (Ma: Figures 3 and 4, Column 11 line 60 – Column 12 line 3, “FIG. 3 illustrates an aerial view of an example scenario 300 in which a group of safety objects designate a new lane that is not associated with traditional lane markings. The example scenario 300 in FIG. 3 depicts a shifted taper, although many other lane modifications exist such as, for example, a flagging taper, shoulder taper, merging taper, one-lane two-way traffic taper, and the like. In some examples, the techniques described herein may determine that all available lanes ( e.g., lanes associated with a same direction of travel as the vehicle) are closed in a scenario like example scenario 300.”, Column 12 lines 4-36, “Example scenario 300 includes a roadway having two directions of traffic, direction of traffic 302 and direction of traffic 304, where each direction of traffic has two lanes associated therewith. Direction of traffic 304 includes two traditional lanes (left traditional lane 306 and right traditional lane 308), as demarcated according to the hashed lane markers and bounded by the double (yellow) line 310 and a roadway extent 312. For the sake of example, it is assumed that the autonomous vehicle 102 has already determined that the right traditional lane 308 is closed (e.g., the autonomous vehicle may have stored and/or maintained a closed status in association with the right traditional lane 308) based at least in part on a collection of safety objects in the roadway (e.g., unlabeled due to their number and represented as circles). However, the autonomous vehicle 102, upon coming upon the shifted taper, may determine that the left traditional lane 306 is closed (e.g., due to the safety objects in that lane) and/or that a lane 314 associated with the (opposite) direction of traffic 302 is unavailable since the direction of traffic 302 is opposite direction of traffic 304 with which the autonomous vehicle 102 is associated and/or because of detecting the double line 314. In other words, by applying the techniques discussed in regard to FIG. 2, the autonomous vehicle 102 may determine that all the (traditional) lane(s) associated with a direction of traffic are blocked (e.g., no traditional lane has an opening wide enough for an autonomous vehicle 102 to pass through according to the techniques discussed above in regard to FIG. 2). The autonomous vehicle 102 may be equipped with additional or alternative techniques for determining an alternative lane shape and/or an open/closed status associated therewith. The alternative lane shape may eschew traditional lane markings in some instances.” Column 12 line 63 – Column 13 line 4, “The autonomous vehicle 102 may generate such a shape to have a contiguous boundary and such that there is at least one lane having a minimum width. The minimum width may be a distance that is greater than the threshold distance (so that the shape generated may avoid including safety objects of an opposite side of the tapered lane). In some examples, an ML model may be trained to determine such a shape ( e.g., based at least in part on a classification task, clustering task, and/or the like).” Column 13 lines 5-16, “In some examples, the autonomous vehicle 102 may repeat this for a second set of safety objects (e.g., the leftward safety objects) associated with a second closed lane status and/or based at least in part on an open lane status. In other words, the autonomous vehicle 102 may inversely determine a shape that positively identifies an open lane, based at least in part on an open lane status and one or more safety objects. In some examples, the autonomous vehicle may detect a safety sign that symbolizes a taper, lane merge, and/or other traffic modification and may determine the alternative lane shape based at least in part on the symbols on the safety sign.”, Column 13 lines 18-51, “FIG. 4 illustrates three alternative lane shapes, a first alternative lane shape 404 associated with a closed lane status, a second alternative lane shape 406 associated with an open lane status, and a third alternative lane shape 408 associated with a closed lane status.”. The cited passages clearly shows that the system determines a first boundary and a second boundary that define an alternate travel path.);
controlling the autonomous vehicle to travel the alternative travel path (Ma: Column 7 lines 1 -32, “In the depicted example, planner may generate and/or select trajectory 136 based at least in part on a closed lane status 134 associated with the current lane 122. Trajectory 136 may comprise instructions for actuating a drive system to cause the autonomous vehicle 102 to merge into an adjacent lane 124 (which may be associated with a same direction of travel and an open lane status 132).”).
Ma does not teach in response to the barrier indicating at least partial closure of the one or more forward travel lanes, filter a variability in position of the one or more objects forming the barrier to determine a first boundary and a second boundary that together define an alternative travel path deviating at least in part from the one or more forward travel lanes;
determining that a minimum width between respective points on the first boundary and the second boundary is at least a width sized for forward travel of the autonomous vehicle; and
in response to a minimum width being at least sized for forward travel of the autonomous vehicle, controlling the autonomous vehicle to travel the alternative travel path.
Zhang, in the same endeavor, teaches in response to the barrier indicating at least partial closure of the one or more forward travel lanes, filter a variability in position of the one or more objects forming the barrier to determine a first boundary and a second boundary that together define an alternative travel path deviating at least in part from the one or more forward travel lanes (Zhang: ¶ 0052, “At block 404, the processor may detect, based on the output from the perception system, one or more objects indicating a presence of a restricted traffic zone. For example, the processor may be configured to detect objects that are used by workers to signal the presence of a restricted traffic zone and/or objects that are typically found in restricted traffic zones. Examples of such objects include cones, roadblocks, barricades, barriers, barrels, emergency lights, emergency signs, emergency vehicles, other types of vehicles, pedestrians, first responders, and construction zone signs.”, ¶ 0060, “At block 508, the processor may generate a drivable area through the restricted traffic zone based on at least one of the prior map 502, the obstruction map 504, and the detected one or more objects. As part of block 508, the processor may also determine whether the autonomous vehicle 105 is entering a restricted traffic zone based on the prior map 502, the obstruction map 504, and the perception output 506. In some implementations, the processor ma also determine the type of the restricted traffic zone (e.g., an emergency zone, a work zone, or a traffic control zone).”, ¶ 0061, “The processor may use one or more of the objects from the perception output 506 in generating the drivable area. For example, the processor may connect a plurality of the objects to form a curve and use the curve as a boundary between the drivable area and a non-drivable area. For example, when the objects include a plurality of cones, the cones may have been placed by a worker to delineate the boundary of the drivable area within the restricted traffic zone. Thus, by connecting the cones into a curve, the processor can generate the boundary between the drivable and non-drivable areas based on the information communicated by the presence of the cones. The processor may determine the boundary using a similar process for other objects that indicate the boundary of the drivable area. For example, the processor may form curves based on the location of roadblocks, barricades, barriers, and/or barrels.”, ¶ 0065, “In a third example, the processor may determine that the curve does not meet the criteria for either of the first and second examples discussed above. In this case, the processor may determine one or more new lanes based on the location of the curve. For example, the processor may identify a new lane substantially parallel to the curve. This new lane may not be fixed to the position of any lane within the prior map 502. The generation of the new lane map may be analogous to shifting the position of the prior lanes and/or "repainting" the lanes based on the location of the curve.”, ¶ 0078, “In certain embodiments, the processor can further be configured to apply a temporal cone filter to reduce false positives associated with detected cones. As described herein, the processor can be configured to determine the boundary of a restricted traffic zone (e.g., a lane closure) based on the detected locations of cones and/or posts. However, under certain circumstances, the processor may detect a cone or post that represents a false positive for the location of the boundary. For example, one or more cone(s) and/or post(s) may have been moved from their initial locations, and thus, the location(s) of the moved cone(s) and/or post(s) may be falsely interpreted as the boundary of the restricted traffic zone. By using the temporal cone filter, the processor can reduce the occurrence of false positives due to change(s) in the locations of cone(s) and/or post(s).”, ¶ 0079, “As part of implementing the temporal cone filter, the processor can divide the roadway into a grid. Depending on the embodiment, the grid may be the same as the grid of the occupancy grid map generated based on the perception output 506. However, in other embodiments, the grid used in the temporal cone filter may be independent of other grids generated by the processor. In some embodiments, the grid can include a coordinate system, such as an east-north-up (ENU) coordinate system, however, aspects of this disclosure are not limited thereto. In one example, the grid may be formed of 1 meter by 1 meter square grid positions.”, ¶ 0080, “The processor can assign each of the cones and/or posts detected in the perception output 506 to a position within the grid. The processor can determine whether the number of cones and/or posts within a given grid position is greater than a threshold number. In response to determining that the number of cones and/or posts within the given grid position is greater than the threshold number, the processor can determine that the grid position forms part of the boundary of the restricted traffic zone ( e.g., the lane closure). When the number of cones and/or posts within the given grid position is equal to or less than the threshold number, the processor can determine that the grid position does not form part of the boundary of the restricted traffic zone ( e.g., the lane closure), in other words, filtering any cones or posts detected at the given grid position. By filtering the cones and/or posts in this way, the processor can reduce the number of false positives described above.”. The cited passages clearly teaches a method of filtering out the variability in position of cones that are used to define the boundaries of the alternate travel lane.);
determining that a minimum width between respective points on the first boundary and the second boundary is at least a width sized for forward travel of the autonomous vehicle (Zhang: ¶ 0017, “In some embodiments, the method further comprises: determining that a minimum width of the drivable area is less than a predetermined width; and in response to determining that the minimum width is less than the predetermined width, determining that the autonomous vehicle should perform the minimum risk condition maneuver.”, ¶ 0052, “At block 404, the processor may detect, based on the output from the perception system, one or more objects indicating a presence of a restricted traffic zone. For example, the processor may be configured to detect objects that are used by workers to signal the presence of a restricted traffic zone and/or objects that are typically found in restricted traffic zones. Examples of such objects include cones, roadblocks, barricades, barriers, barrels, emergency lights, emergency signs, emergency vehicles, other types of vehicles, pedestrians, first responders, and construction zone signs.”, ¶ 0060, “At block 508, the processor may generate a drivable area through the restricted traffic zone based on at least one of the prior map 502, the obstruction map 504, and the detected one or more objects. As part of block 508, the processor may also determine whether the autonomous vehicle 105 is entering a restricted traffic zone based on the prior map 502, the obstruction map 504, and the perception output 506. In some implementations, the processor ma also determine the type of the restricted traffic zone (e.g., an emergency zone, a work zone, or a traffic control zone).”, ¶ 0061, “The processor may use one or more of the objects from the perception output 506 in generating the drivable area. For example, the processor may connect a plurality of the objects to form a curve and use the curve as a boundary between the drivable area and a non-drivable area. For example, when the objects include a plurality of cones, the cones may have been placed by a worker to delineate the boundary of the drivable area within the restricted traffic zone. Thus, by connecting the cones into a curve, the processor can generate the boundary between the drivable and non-drivable areas based on the information communicated by the presence of the cones. The processor may determine the boundary using a similar process for other objects that indicate the boundary of the drivable area. For example, the processor may form curves based on the location of roadblocks, barricades, barriers, and/or barrels.”, ¶ 0065, “In a third example, the processor may determine that the curve does not meet the criteria for either of the first and second examples discussed above. In this case, the processor may determine one or more new lanes based on the location of the curve. For example, the processor may identify a new lane substantially parallel to the curve. This new lane may not be fixed to the position of any lane within the prior map 502. The generation of the new lane map may be analogous to shifting the position of the prior lanes and/or "repainting" the lanes based on the location of the curve.”, ¶ 0068, “In some implementations, one or more of the requirements used for determining whether the autonomous vehicle 105 can drive through the restricted traffic zone may vary depending on the current conditions of the environment. For example, the requirements can include one or more of the following: current traffic conditions, a current speed of the autonomous vehicle 105, a current perception range of the perception system, and a minimum width of the drivable area.”, ¶ 0069, “For example, the processor can determine whether a minimum width of the drivable area is less than a predetermined width. In response to determining that the minimum width is less than the predetermined width, the processor may determine that the autonomous vehicle 105 should perform the MRC maneuver. In some implementations, the predetermined width may be 3 meters, however, this disclosure is not limited thereto and the predetermined width may be less than or greater than 3 meters. Depending on the embodiment, the predetermined width may be based on a width of the autonomous vehicle 105.”, ¶ 0070, “In response to determining that the autonomous vehicle 105 cannot drive through the restricted traffic zone, the processor may cause the autonomous vehicle 105 to perform the MRC maneuver. The processor may also determine whether the MRC maneuver should include the autonomous vehicle 105 stopping in a current lane, stopping in an emergency lane, or stopping on a different portion of the drivable area based on the drivable area.”. The cited passages clearly shows that the system is configured to determine if the minimum width of the drivable area defined by the detected objects is at or above a threshold width that indicates the area is suitable for the vehicle to travel through.); and
in response to a minimum width being at least sized for forward travel of the autonomous vehicle, controlling the autonomous vehicle to travel the alternative travel path (Zhang: ¶ 0017, “In some embodiments, the method further comprises: determining that a minimum width of the drivable area is less than a predetermined width; and in response to determining that the minimum width is less than the predetermined width, determining that the autonomous vehicle should perform the minimum risk condition maneuver.”, ¶ 0069, “For example, the processor can determine whether a minimum width of the drivable area is less than a predetermined width. In response to determining that the minimum width is less than the predetermined width, the processor may determine that the autonomous vehicle 105 should perform the MRC maneuver. In some implementations, the predetermined width may be 3 meters, however, this disclosure is not limited thereto and the predetermined width may be less than or greater than 3 meters. Depending on the embodiment, the predetermined width may be based on a width of the autonomous vehicle 105.”, ¶ 0070, “In response to determining that the autonomous vehicle 105 cannot drive through the restricted traffic zone, the processor may cause the autonomous vehicle 105 to perform the MRC maneuver. The processor may also determine whether the MRC maneuver should include the autonomous vehicle 105 stopping in a current lane, stopping in an emergency lane, or stopping on a different portion of the drivable area based on the drivable area.”. One of ordinary skill in the art would recognize that if the minimum width of the drivable area is at or above the threshold, the vehicle will travel through the drivable area.).
Ma teaches a method for path determination, the method comprising: receiving sensor data from one or more sensors of an autonomous vehicle; identifying, based on the sensor data, (i) one or more forward travel lanes and (ii) one or more objects; determining the one or more objects form a barrier indicating at least partial closure of the one or more forward travel lanes; determine a first boundary and a second boundary that together define an alternative travel path deviating at least in part from the one or more forward travel lanes; controlling the autonomous vehicle to travel the alternative travel path. Ma does not teach in response to the barrier indicating at least partial closure of the one or more forward travel lanes, filter a variability in position of the one or more objects forming the barrier to determine a first boundary and a second boundary that together define an alternative travel path deviating at least in part from the one or more forward travel lanes; determining that a minimum width between respective points on the first boundary and the second boundary is at least a width sized for forward travel of the autonomous vehicle; and in response to a minimum width being at least sized for forward travel of the autonomous vehicle, controlling the autonomous vehicle to travel the alternative travel path. Zhang teaches in response to the barrier indicating at least partial closure of the one or more forward travel lanes, filter a variability in position of the one or more objects forming the barrier to determine a first boundary and a second boundary that together define an alternative travel path deviating at least in part from the one or more forward travel lanes; determining that a minimum width between respective points on the first boundary and the second boundary is at least a width sized for forward travel of the autonomous vehicle; and in response to a minimum width being at least sized for forward travel of the autonomous vehicle, controlling the autonomous vehicle to travel the alternative travel path. A person of ordinary skill in the art would have had the technological capabilities required to have modified the method taught in Ma with in response to the barrier indicating at least partial closure of the one or more forward travel lanes, filter a variability in position of the one or more objects forming the barrier to determine a first boundary and a second boundary that together define an alternative travel path deviating at least in part from the one or more forward travel lanes; determining that a minimum width between respective points on the first boundary and the second boundary is at least a width sized for forward travel of the autonomous vehicle; and in response to a minimum width being at least sized for forward travel of the autonomous vehicle, controlling the autonomous vehicle to travel the alternative travel path taught in Zhang. Furthermore, the path determination method taught in Ma is already configured to determine if the space between detected objects along a vehicles path meet a width threshold such that the vehicle can pass through them and controls the vehicle to travel the alternat path, so modifying the method such that it determines if the alternated travel path meets a width threshold using the method taught in Zhang would not change or introduce new functionality. Additionally, the method taught in Ma is already configured to detect a plurality of objects that indicate a lane closure and use these object to define a boundary indicating a drivable area. As such, one of ordinary skill in the art would have been able to implement the filtering method taught in Zhang according to known methods. Ma is already configured to detect objects and define boundaries using said objects, so one of ordinary skill in the art would have been able to simply add the filtering method taught in Zhang. Such modifications would not have changed or introduced new functionality. No inventive effort would have been required. The combination would have yielded the predictable result of a method for path determination comprising in response to the barrier indicating at least partial closure of the one or more forward travel lanes, filter a variability in position of the one or more objects forming the barrier to determine a first boundary and a second boundary that together define an alternative travel path deviating at least in part from the one or more forward travel lanes; determining that a minimum width between respective points on the first boundary and the second boundary is at least a width sized for forward travel of the autonomous vehicle; and in response to a minimum width being at least sized for forward travel of the autonomous vehicle, controlling the autonomous vehicle to travel the alternative travel path.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have combine the method taught in Ma with in response to the barrier indicating at least partial closure of the one or more forward travel lanes, filter a variability in position of the one or more objects forming the barrier to determine a first boundary and a second boundary that together define an alternative travel path deviating at least in part from the one or more forward travel lanes; determining that a minimum width between respective points on the first boundary and the second boundary is at least a width sized for forward travel of the autonomous vehicle; and in response to a minimum width being at least sized for forward travel of the autonomous vehicle, controlling the autonomous vehicle to travel the alternative travel path taught in Zhang with a reasonable expectation of success. One of ordinary skill in the art would have made this modification because the combination would have yielded predictable results.
Regarding claim 10, Ma in view of Zhang teaches further comprising: while controlling the autonomous vehicle to travel the alternative travel path, identify an end of the closure of all of the one or more forward travel lanes (Ma: Column 10 lines 22-36, “Turning to FIG. 2D, at operation 232, example process 200 may comprise determining that sensor data indicates an absence of a safety object associated with a ( closed) lane for a duration of time that meets or exceeds a threshold duration, according to any of the techniques discussed herein. For example, example process 200 may comprise tracking a duration of time that no new object detections are received that identify an object and/or a safety object in a closed lane. That duration of time may be reset any time a new object detection identifies such an object (example process 200 may continue to operation 230 in such an instance). For example, the status associated with a closed lane may thereby be set to an open lane status shortly after an autonomous vehicle clears a last traffic cone, as depicted in FIG. 2D.”. The cited passage clearly shows that the method determines an end of the lane closure.);
control the autonomous vehicle to travel in one of the one or more forward travel lanes at the end of the closure (Ma: Column 7 lines 1-32, “The planner 112 may use the perception data, including the lane closed/open states discussed herein, to determine one or more trajectories to control the autonomous vehicle 102 to traverse a path or route and/or otherwise control operation of the autonomous vehicle 102, though any such operation may be performed in various other components. For example, the planner 112 may determine a route for the autonomous vehicle 102 from a first location to a second location; generate, substantially simultaneously, a plurality of potential trajectories for controlling motion of the autonomous vehicle 102 in accordance with a receding horizon technique ( e.g., 1 micro-second, half a second, every 10 seconds, and the like) and based at least in part on the lane states 130 (which may be associated with the map 116 and/or state tracker 118) to traverse the route ( e.g., in order to avoid any of the detected objects and/or to avoid operating in a closed lane); and select one of the potential trajectories as a trajectory 136 of the autonomous vehicle 102 that may be used to generate a drive control signal that may be transmitted to drive components of the autonomous vehicle 102.”. The system taught in Ma is clearly configured to control the vehicle to travel in the open travel lanes.).
Regarding claim 11, Ma in view of Zhang teaches further comprising: determine the one or more forward travel lanes includes a plurality of forward travel lanes (Ma: Column 6 lines 9-23, “In some examples, if at least one of the object detections generated by the perception engine 114 indicates a classification associated with a safety object (i.e., a "safety class"), the perception engine 114 may trigger a lane closure analysis. In an additional or alternate example, the autonomous vehicle 102 may analyze at least a current lane 122; any adjacent lane(s), such as adjacent lane 124; and/or any other lane to determine whether the lane is open or closed. For example, the perception engine 114 may continuously or periodically conduct the lane closure analysis described herein, regardless of whether a safety object has been detected, and/or if a safety object is detected the perception engine 114 may trigger a lane analysis in addition to a periodic lane analysis.”. As can be seen in the cited passage, the perception engine is configured to identify the travel lanes of the road the vehicle is traveling.); and
when the barrier indicates closure of less than all of the plurality of forward travel lanes, control the autonomous vehicle to travel in an open travel lane of the plurality of forward travel lanes (Ma: Column 7 lines 1 -32, “In the depicted example, planner may generate and/or select trajectory 136 based at least in part on a closed lane status 134 associated with the current lane 122. Trajectory 136 may comprise instructions for actuating a drive system to cause the autonomous vehicle 102 to merge into an adjacent lane 124 (which may be associated with a same direction of travel and an open lane status 132).”. The cited passage clearly teaches controlling the vehicle to travel in an open travel lane when not all of the travel lanes are closed.).
Regarding claim 15, Ma teaches an autonomous vehicle comprising (Ma: Column 13 lines 53-67, “FIG. 5 illustrates a block diagram of an example system that implements the techniques discussed herein. In some instances, the system 500 may include a vehicle 502, which may represent the autonomous vehicle 102 in FIG. 1.”):
one or more sensors (Ma: Column 14 lines 4-20, “In some instances, the sensor(s) 506 may include lidar sensors, radar sensors, ultrasonic transducers, sonar sensors, location sensors (e.g., global positioning system (GPS), compass), inertial sensors ( e.g., inertial measurement units (IMUs), accelerometers, magnetometers, gyroscopes), image sensors (e.g., red-green-blue (RGB), infrared (IR), intensity, depth, time of flight cameras), microphones, wheel encoders, environment sensors (e.g., thermometer, hygrometer, light sensors, pressure sensors), etc. The sensor(s) 506 may include multiple instances of each of these or other types of sensors. For instance, the radar sensors may include individual radar sensors located at the comers, front, back, sides, and/or top of the vehicle 502. As another example, the cameras may include multiple cameras disposed at various locations about the exterior and/or interior of the vehicle 502. The sensor(s) 506 may provide input to the vehicle computing device(s) 504 and/or to computing device(s) 514.”); and
a path determination system comprising a processor and a memory, the processor configured to (Ma: Figure 5 computing device 504, Column 15 lines 23-50, “Additionally, the drive component(s) 512 may include a drive component controller which may receive and preprocess data from the sensor(s) and to control operation of the various vehicle systems. In some instances, the drive component controller may include one or more processors and memory communicatively coupled with the one or more processors. The memory may store one or more components to perform various functionalities of the drive component(s) 512.”, Column 15 lines 51-65, “The vehicle computing device(s) 504 may include processor(s) 518 and memory 520 communicatively coupled with the one or more processors 518. Computing device(s) 514 may also include processor(s) 522, and/or memory 524. The processor(s) 518 and/or 522 may be any suitable processor capable of executing instructions to process data and perform operations as described herein.”):
receive sensor data from the one or more sensors (Ma: Column 4 lines 39-52, “According to the techniques discussed herein, the autonomous vehicle 102 may receive sensor data from sensor(s) 104 of the autonomous vehicle 102. For example, the sensor(s) 104 may include a location sensor (e.g., a global positioning system (GPS) sensor), an inertia sensor (e.g., an accelerometer sensor, a gyroscope sensor, etc.), a magnetic field sensor (e.g., a compass), a position/velocity/acceleration sensor (e.g., a speedometer, a drive system sensor), a depth position sensor ( e.g., a lidar sensor, a radar sensor, a sonar sensor, a time of flight (ToF) camera, a depth camera), an image sensor (e.g., a visible light spectrum camera, a depth camera, an infrared camera), an audio sensor (e.g., a microphone), and/or environmental sensor (e.g., a barometer, a hygrometer, etc.).”);
identify, based on the sensor data, (i) one or more forward travel lanes and (ii) one or more objects (Ma: Column 5 lines 39-48, “The perception engine 114 may receive sensor data from sensor( s) 104 and may determine perception data therefrom. For example, perception engine 114 may include one or more machine-learned (ML) models and/or other computer executable instructions for detecting, identifying, segmenting, classifying, and/or tracking objects from sensor data collected from the environment of the autonomous vehicle 102. In some examples, the perception engine 114 may comprise a component for detecting whether a lane is open or closed.”, Column 5 lines 49-62, “In the illustrated example scenario 100, autonomous vehicle 102 may receive sensor data from one or more of the sensor(s) 104 as the autonomous vehicle 102 approaches a collection of traffic cones 120. Traffic cones 120 may be one example of safety objects associated with a lane closure. The perception engine 114 may comprise one or more ML models for detecting, based at least in part on the sensor data, object(s) in the environment surrounding the autonomous vehicle 102 and/or classifying the object(s). For example, the autonomous vehicle 102 may receive an image and/or point cloud data (e.g., data from lidar, radar, sonar), which the autonomous vehicle 102 may determine is associated with one or more safety objects (e.g., by determining an object detection is associated with a safety class).”, Column 6 lines 9-23, “In some examples, if at least one of the object detections generated by the perception engine 114 indicates a classification associated with a safety object (i.e., a "safety class"), the perception engine 114 may trigger a lane closure analysis. In an additional or alternate example, the autonomous vehicle 102 may analyze at least a current lane 122; any adjacent lane(s), such as adjacent lane 124; and/or any other lane to determine whether the lane is open or closed. For example, the perception engine 114 may continuously or periodically conduct the lane closure analysis described herein, regardless of whether a safety object has been detected, and/or if a safety object is detected the perception engine 114 may trigger a lane analysis in addition to a periodic lane analysis.”. As can be seen from the cited passages, the system is clearly configured to detect one or more objects based the sensor data. Additionally, as can be seen in Column 6 lines 9-23, the perception engine is configured to identify the travel lanes of the road the vehicle is traveling.);
determine the one or more objects form a at leadt partial barrier indicating closure of the one or more forward travel lanes (Ma: Column 9 lines 9-24, “At operation 220, example process 200 may comprise determining whether a distance between a dilated object detection another object detection, another dilated object detection, and/or an extent of a lane and/or roadway meets or exceeds a distance threshold, according to any of the techniques discussed herein. In some examples, the distance threshold may correspond to a width and/or length of the autonomous vehicle ( e.g., depending on the dimension in which the distance was measured-in the depicted example, the distance threshold may be based at least in part on a width of the autonomous vehicle) and/or a tolerance. Operation 220 may functionally determine whether the autonomous vehicle would fit between dilated object detections (along a longitudinal or lateral axis of the vehicle, for example). If the distance is less than the distance threshold, example process 200 may continue to operation 222.”, Column 9 lines 25-44, “At operation 222, example process 200 may comprise determining a closed status indicating that the lane is closed, according to any of the techniques discussed herein. In some examples, operation 222 may comprise any method of setting and/or saving a state in association with the analyzed lane such as, for example, flipping a flag in a register, transitioning a state machine to a state to identify the lane as being closed. For example, FIG. 2A depicts lane states 224, which comprise identifying an analyzed lane as being closed. In some instances, the status may be associated with a portion of a lane based at least in part on a dilated object detection and/or object detection closest to the autonomous vehicle.”. The cited passages teach determining if the detected objects are spaced in such a way that the vehicle can fit between them and if the space between the objects ca not allow the vehicle to pass, setting the lane to a closed status. This is clearly a method of determining if the objects create a blockage or barrier on the road.);
determine a first boundary and a second boundary that together define an alternative travel path deviating at least in part from the one or more forward travel lanes (Ma: Figures 3 and 4, Column 11 line 60 – Column 12 line 3, “FIG. 3 illustrates an aerial view of an example scenario 300 in which a group of safety objects designate a new lane that is not associated with traditional lane markings. The example scenario 300 in FIG. 3 depicts a shifted taper, although many other lane modifications exist such as, for example, a flagging taper, shoulder taper, merging taper, one-lane two-way traffic taper, and the like. In some examples, the techniques described herein may determine that all available lanes ( e.g., lanes associated with a same direction of travel as the vehicle) are closed in a scenario like example scenario 300.”, Column 12 lines 4-36, “Example scenario 300 includes a roadway having two directions of traffic, direction of traffic 302 and direction of traffic 304, where each direction of traffic has two lanes associated therewith. Direction of traffic 304 includes two traditional lanes (left traditional lane 306 and right traditional lane 308), as demarcated according to the hashed lane markers and bounded by the double (yellow) line 310 and a roadway extent 312. For the sake of example, it is assumed that the autonomous vehicle 102 has already determined that the right traditional lane 308 is closed (e.g., the autonomous vehicle may have stored and/or maintained a closed status in association with the right traditional lane 308) based at least in part on a collection of safety objects in the roadway (e.g., unlabeled due to their number and represented as circles). However, the autonomous vehicle 102, upon coming upon the shifted taper, may determine that the left traditional lane 306 is closed (e.g., due to the safety objects in that lane) and/or that a lane 314 associated with the (opposite) direction of traffic 302 is unavailable since the direction of traffic 302 is opposite direction of traffic 304 with which the autonomous vehicle 102 is associated and/or because of detecting the double line 314. In other words, by applying the techniques discussed in regard to FIG. 2, the autonomous vehicle 102 may determine that all the (traditional) lane(s) associated with a direction of traffic are blocked (e.g., no traditional lane has an opening wide enough for an autonomous vehicle 102 to pass through according to the techniques discussed above in regard to FIG. 2). The autonomous vehicle 102 may be equipped with additional or alternative techniques for determining an alternative lane shape and/or an open/closed status associated therewith. The alternative lane shape may eschew traditional lane markings in some instances.” Column 12 line 63 – Column 13 line 4, “The autonomous vehicle 102 may generate such a shape to have a contiguous boundary and such that there is at least one lane having a minimum width. The minimum width may be a distance that is greater than the threshold distance (so that the shape generated may avoid including safety objects of an opposite side of the tapered lane). In some examples, an ML model may be trained to determine such a shape ( e.g., based at least in part on a classification task, clustering task, and/or the like).” Column 13 lines 5-16, “In some examples, the autonomous vehicle 102 may repeat this for a second set of safety objects (e.g., the leftward safety objects) associated with a second closed lane status and/or based at least in part on an open lane status. In other words, the autonomous vehicle 102 may inversely determine a shape that positively identifies an open lane, based at least in part on an open lane status and one or more safety objects. In some examples, the autonomous vehicle may detect a safety sign that symbolizes a taper, lane merge, and/or other traffic modification and may determine the alternative lane shape based at least in part on the symbols on the safety sign.”, Column 13 lines 18-51, “FIG. 4 illustrates three alternative lane shapes, a first alternative lane shape 404 associated with a closed lane status, a second alternative lane shape 406 associated with an open lane status, and a third alternative lane shape 408 associated with a closed lane status.”. The cited passages clearly shows that the system determines a first boundary and a second boundary that define an alternate travel path.);
control the autonomous vehicle to travel the alternative travel path (Ma: Column 7 lines 1 -32, “In the depicted example, planner may generate and/or select trajectory 136 based at least in part on a closed lane status 134 associated with the current lane 122. Trajectory 136 may comprise instructions for actuating a drive system to cause the autonomous vehicle 102 to merge into an adjacent lane 124 (which may be associated with a same direction of travel and an open lane status 132).”).
Ma does not teach in response to the barrier indicating at least partial closure of the one or more forward travel lanes, filter a variability in position of the one or more objects forming the barrier to determine a first boundary and a second boundary that together define an alternative travel path deviating at least in part from the one or more forward travel lanes;
determine that a minimum width between respective points on the first boundary and the second boundary is at least a width sized for forward travel of the autonomous vehicle; and
in response to a minimum width being at least sized for forward travel of the autonomous vehicle, control the autonomous vehicle to travel the alternative travel path.
Zhang, in the same endeavor, teaches in response to the barrier indicating at least partial closure of the one or more forward travel lanes, filter a variability in position of the one or more objects forming the barrier to determine a first boundary and a second boundary that together define an alternative travel path deviating at least in part from the one or more forward travel lanes (Zhang: ¶ 0052, “At block 404, the processor may detect, based on the output from the perception system, one or more objects indicating a presence of a restricted traffic zone. For example, the processor may be configured to detect objects that are used by workers to signal the presence of a restricted traffic zone and/or objects that are typically found in restricted traffic zones. Examples of such objects include cones, roadblocks, barricades, barriers, barrels, emergency lights, emergency signs, emergency vehicles, other types of vehicles, pedestrians, first responders, and construction zone signs.”, ¶ 0060, “At block 508, the processor may generate a drivable area through the restricted traffic zone based on at least one of the prior map 502, the obstruction map 504, and the detected one or more objects. As part of block 508, the processor may also determine whether the autonomous vehicle 105 is entering a restricted traffic zone based on the prior map 502, the obstruction map 504, and the perception output 506. In some implementations, the processor ma also determine the type of the restricted traffic zone (e.g., an emergency zone, a work zone, or a traffic control zone).”, ¶ 0061, “The processor may use one or more of the objects from the perception output 506 in generating the drivable area. For example, the processor may connect a plurality of the objects to form a curve and use the curve as a boundary between the drivable area and a non-drivable area. For example, when the objects include a plurality of cones, the cones may have been placed by a worker to delineate the boundary of the drivable area within the restricted traffic zone. Thus, by connecting the cones into a curve, the processor can generate the boundary between the drivable and non-drivable areas based on the information communicated by the presence of the cones. The processor may determine the boundary using a similar process for other objects that indicate the boundary of the drivable area. For example, the processor may form curves based on the location of roadblocks, barricades, barriers, and/or barrels.”, ¶ 0065, “In a third example, the processor may determine that the curve does not meet the criteria for either of the first and second examples discussed above. In this case, the processor may determine one or more new lanes based on the location of the curve. For example, the processor may identify a new lane substantially parallel to the curve. This new lane may not be fixed to the position of any lane within the prior map 502. The generation of the new lane map may be analogous to shifting the position of the prior lanes and/or "repainting" the lanes based on the location of the curve.”, ¶ 0078, “In certain embodiments, the processor can further be configured to apply a temporal cone filter to reduce false positives associated with detected cones. As described herein, the processor can be configured to determine the boundary of a restricted traffic zone (e.g., a lane closure) based on the detected locations of cones and/or posts. However, under certain circumstances, the processor may detect a cone or post that represents a false positive for the location of the boundary. For example, one or more cone(s) and/or post(s) may have been moved from their initial locations, and thus, the location(s) of the moved cone(s) and/or post(s) may be falsely interpreted as the boundary of the restricted traffic zone. By using the temporal cone filter, the processor can reduce the occurrence of false positives due to change(s) in the locations of cone(s) and/or post(s).”, ¶ 0079, “As part of implementing the temporal cone filter, the processor can divide the roadway into a grid. Depending on the embodiment, the grid may be the same as the grid of the occupancy grid map generated based on the perception output 506. However, in other embodiments, the grid used in the temporal cone filter may be independent of other grids generated by the processor. In some embodiments, the grid can include a coordinate system, such as an east-north-up (ENU) coordinate system, however, aspects of this disclosure are not limited thereto. In one example, the grid may be formed of 1 meter by 1 meter square grid positions.”, ¶ 0080, “The processor can assign each of the cones and/or posts detected in the perception output 506 to a position within the grid. The processor can determine whether the number of cones and/or posts within a given grid position is greater than a threshold number. In response to determining that the number of cones and/or posts within the given grid position is greater than the threshold number, the processor can determine that the grid position forms part of the boundary of the restricted traffic zone ( e.g., the lane closure). When the number of cones and/or posts within the given grid position is equal to or less than the threshold number, the processor can determine that the grid position does not form part of the boundary of the restricted traffic zone ( e.g., the lane closure), in other words, filtering any cones or posts detected at the given grid position. By filtering the cones and/or posts in this way, the processor can reduce the number of false positives described above.”. The cited passages clearly teaches a method of filtering out the variability in position of cones that are used to define the boundaries of the alternate travel lane.);
determine that a minimum width between respective points on the first boundary and the second boundary is at least a width sized for forward travel of the autonomous vehicle (Zhang: ¶ 0017, “In some embodiments, the method further comprises: determining that a minimum width of the drivable area is less than a predetermined width; and in response to determining that the minimum width is less than the predetermined width, determining that the autonomous vehicle should perform the minimum risk condition maneuver.”, ¶ 0052, “At block 404, the processor may detect, based on the output from the perception system, one or more objects indicating a presence of a restricted traffic zone. For example, the processor may be configured to detect objects that are used by workers to signal the presence of a restricted traffic zone and/or objects that are typically found in restricted traffic zones. Examples of such objects include cones, roadblocks, barricades, barriers, barrels, emergency lights, emergency signs, emergency vehicles, other types of vehicles, pedestrians, first responders, and construction zone signs.”, ¶ 0060, “At block 508, the processor may generate a drivable area through the restricted traffic zone based on at least one of the prior map 502, the obstruction map 504, and the detected one or more objects. As part of block 508, the processor may also determine whether the autonomous vehicle 105 is entering a restricted traffic zone based on the prior map 502, the obstruction map 504, and the perception output 506. In some implementations, the processor ma also determine the type of the restricted traffic zone (e.g., an emergency zone, a work zone, or a traffic control zone).”, ¶ 0061, “The processor may use one or more of the objects from the perception output 506 in generating the drivable area. For example, the processor may connect a plurality of the objects to form a curve and use the curve as a boundary between the drivable area and a non-drivable area. For example, when the objects include a plurality of cones, the cones may have been placed by a worker to delineate the boundary of the drivable area within the restricted traffic zone. Thus, by connecting the cones into a curve, the processor can generate the boundary between the drivable and non-drivable areas based on the information communicated by the presence of the cones. The processor may determine the boundary using a similar process for other objects that indicate the boundary of the drivable area. For example, the processor may form curves based on the location of roadblocks, barricades, barriers, and/or barrels.”, ¶ 0065, “In a third example, the processor may determine that the curve does not meet the criteria for either of the first and second examples discussed above. In this case, the processor may determine one or more new lanes based on the location of the curve. For example, the processor may identify a new lane substantially parallel to the curve. This new lane may not be fixed to the position of any lane within the prior map 502. The generation of the new lane map may be analogous to shifting the position of the prior lanes and/or "repainting" the lanes based on the location of the curve.”, ¶ 0068, “In some implementations, one or more of the requirements used for determining whether the autonomous vehicle 105 can drive through the restricted traffic zone may vary depending on the current conditions of the environment. For example, the requirements can include one or more of the following: current traffic conditions, a current speed of the autonomous vehicle 105, a current perception range of the perception system, and a minimum width of the drivable area.”, ¶ 0069, “For example, the processor can determine whether a minimum width of the drivable area is less than a predetermined width. In response to determining that the minimum width is less than the predetermined width, the processor may determine that the autonomous vehicle 105 should perform the MRC maneuver. In some implementations, the predetermined width may be 3 meters, however, this disclosure is not limited thereto and the predetermined width may be less than or greater than 3 meters. Depending on the embodiment, the predetermined width may be based on a width of the autonomous vehicle 105.”, ¶ 0070, “In response to determining that the autonomous vehicle 105 cannot drive through the restricted traffic zone, the processor may cause the autonomous vehicle 105 to perform the MRC maneuver. The processor may also determine whether the MRC maneuver should include the autonomous vehicle 105 stopping in a current lane, stopping in an emergency lane, or stopping on a different portion of the drivable area based on the drivable area.”. The cited passages clearly shows that the system is configured to determine if the minimum width of the drivable area defined by the detected objects is at or above a threshold width that indicates the area is suitable for the vehicle to travel through.); and
in response to a minimum width being at least sized for forward travel of the autonomous vehicle, control the autonomous vehicle to travel the alternative travel path (Zhang: ¶ 0017, “In some embodiments, the method further comprises: determining that a minimum width of the drivable area is less than a predetermined width; and in response to determining that the minimum width is less than the predetermined width, determining that the autonomous vehicle should perform the minimum risk condition maneuver.”, ¶ 0069, “For example, the processor can determine whether a minimum width of the drivable area is less than a predetermined width. In response to determining that the minimum width is less than the predetermined width, the processor may determine that the autonomous vehicle 105 should perform the MRC maneuver. In some implementations, the predetermined width may be 3 meters, however, this disclosure is not limited thereto and the predetermined width may be less than or greater than 3 meters. Depending on the embodiment, the predetermined width may be based on a width of the autonomous vehicle 105.”, ¶ 0070, “In response to determining that the autonomous vehicle 105 cannot drive through the restricted traffic zone, the processor may cause the autonomous vehicle 105 to perform the MRC maneuver. The processor may also determine whether the MRC maneuver should include the autonomous vehicle 105 stopping in a current lane, stopping in an emergency lane, or stopping on a different portion of the drivable area based on the drivable area.”. One of ordinary skill in the art would recognize that if the minimum width of the drivable area is at or above the threshold, the vehicle will travel through the drivable area.).
Ma teaches an autonomous vehicle comprising a processor and a memory, the processor configured to: receive sensor data from one or more sensors of an autonomous vehicle; identify, based on the sensor data, (i) one or more forward travel lanes and (ii) one or more objects; determine the one or more objects form a barrier indicating at least partial closure of the one or more forward travel lanes; determine a first boundary and a second boundary that together define an alternative travel path deviating at least in part from the one or more forward travel lanes; control the autonomous vehicle to travel the alternative travel path. Ma does not teach in response to the barrier indicating at least partial closure of the one or more forward travel lanes, filter a variability in position of the one or more objects forming the barrier to determine a first boundary and a second boundary that together define an alternative travel path deviating at least in part from the one or more forward travel lanes; determine that a minimum width between respective points on the first boundary and the second boundary is at least a width sized for forward travel of the autonomous vehicle; and in response to a minimum width being at least sized for forward travel of the autonomous vehicle, control the autonomous vehicle to travel the alternative travel path. Zhang teaches in response to the barrier indicating at least partial closure of the one or more forward travel lanes, filter a variability in position of the one or more objects forming the barrier to determine a first boundary and a second boundary that together define an alternative travel path deviating at least in part from the one or more forward travel lanes; determine that a minimum width between respective points on the first boundary and the second boundary is at least a width sized for forward travel of the autonomous vehicle; and in response to a minimum width being at least sized for forward travel of the autonomous vehicle, control the autonomous vehicle to travel the alternative travel path. A person of ordinary skill in the art would have had the technological capabilities required to have modified the system taught in Ma with in response to the barrier indicating at least partial closure of the one or more forward travel lanes, filter a variability in position of the one or more objects forming the barrier to determine a first boundary and a second boundary that together define an alternative travel path deviating at least in part from the one or more forward travel lanes; determine that a minimum width between respective points on the first boundary and the second boundary is at least a width sized for forward travel of the autonomous vehicle; and in response to a minimum width being at least sized for forward travel of the autonomous vehicle, control the autonomous vehicle to travel the alternative travel path taught in Zhang. Furthermore, the path determination system taught in Ma is already configured to determine if the space between detected objects along a vehicles path meet a width threshold such that the vehicle can pass through them and controls the vehicle to travel the alternat path, so modifying the system such that it determines if the alternated travel path meets a width threshold using the method taught in Zhang would not change or introduce new functionality. Additionally, the system taught in Ma is already configured to detect a plurality of objects that indicate a lane closure and use these object to define a boundary indicating a drivable area. As such, one of ordinary skill in the art would have been able to implement the filtering method taught in Zhang according to known methods. Ma is already configured to detect objects and define boundaries using said objects, so one of ordinary skill in the art would have been able to simply add the filtering method taught in Zhang. Such modifications would not have changed or introduced new functionality. No inventive effort would have been required. The combination would have yielded the predictable result of an autonomous vehicle comprising in response to the barrier indicating at least partial closure of the one or more forward travel lanes, filter a variability in position of the one or more objects forming the barrier to determine a first boundary and a second boundary that together define an alternative travel path deviating at least in part from the one or more forward travel lanes; determine that a minimum width between respective points on the first boundary and the second boundary is at least a width sized for forward travel of the autonomous vehicle; and in response to a minimum width being at least sized for forward travel of the autonomous vehicle, control the autonomous vehicle to travel the alternative travel path.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have combine the system taught in Ma with in response to the barrier indicating at least partial closure of the one or more forward travel lanes, filter a variability in position of the one or more objects forming the barrier to determine a first boundary and a second boundary that together define an alternative travel path deviating at least in part from the one or more forward travel lanes; determine that a minimum width between respective points on the first boundary and the second boundary is at least a width sized for forward travel of the autonomous vehicle; and in response to a minimum width being at least sized for forward travel of the autonomous vehicle, control the autonomous vehicle to travel the alternative travel path taught in Zhang with a reasonable expectation of success. One of ordinary skill in the art would have made this modification because the combination would have yielded predictable results.
Regarding claim 17, Ma in view of Zhang teaches wherein the processor is further configured to: while controlling the autonomous vehicle to travel the alternative travel path, identify an end of the closure of all of the one or more forward travel lanes (Ma: Column 10 lines 22-36, “Turning to FIG. 2D, at operation 232, example process 200 may comprise determining that sensor data indicates an absence of a safety object associated with a ( closed) lane for a duration of time that meets or exceeds a threshold duration, according to any of the techniques discussed herein. For example, example process 200 may comprise tracking a duration of time that no new object detections are received that identify an object and/or a safety object in a closed lane. That duration of time may be reset any time a new object detection identifies such an object (example process 200 may continue to operation 230 in such an instance). For example, the status associated with a closed lane may thereby be set to an open lane status shortly after an autonomous vehicle clears a last traffic cone, as depicted in FIG. 2D.”. The cited passage clearly shows that the method determines an end of the lane closure.);
control the autonomous vehicle to travel in one of the one or more forward travel lanes at the end of the closure (Ma: Column 7 lines 1-32, “The planner 112 may use the perception data, including the lane closed/open states discussed herein, to determine one or more trajectories to control the autonomous vehicle 102 to traverse a path or route and/or otherwise control operation of the autonomous vehicle 102, though any such operation may be performed in various other components. For example, the planner 112 may determine a route for the autonomous vehicle 102 from a first location to a second location; generate, substantially simultaneously, a plurality of potential trajectories for controlling motion of the autonomous vehicle 102 in accordance with a receding horizon technique ( e.g., 1 micro-second, half a second, every 10 seconds, and the like) and based at least in part on the lane states 130 (which may be associated with the map 116 and/or state tracker 118) to traverse the route ( e.g., in order to avoid any of the detected objects and/or to avoid operating in a closed lane); and select one of the potential trajectories as a trajectory 136 of the autonomous vehicle 102 that may be used to generate a drive control signal that may be transmitted to drive components of the autonomous vehicle 102.”. The system taught in Ma is clearly configured to control the vehicle to travel in the open travel lanes.).
Regarding claim 18, Ma in view of Zhang teaches wherein the processor is further configured to: determine the one or more forward travel lanes includes a plurality of forward travel lanes (Ma: Column 6 lines 9-23, “In some examples, if at least one of the object detections generated by the perception engine 114 indicates a classification associated with a safety object (i.e., a "safety class"), the perception engine 114 may trigger a lane closure analysis. In an additional or alternate example, the autonomous vehicle 102 may analyze at least a current lane 122; any adjacent lane(s), such as adjacent lane 124; and/or any other lane to determine whether the lane is open or closed. For example, the perception engine 114 may continuously or periodically conduct the lane closure analysis described herein, regardless of whether a safety object has been detected, and/or if a safety object is detected the perception engine 114 may trigger a lane analysis in addition to a periodic lane analysis.”. As can be seen in the cited passage, the perception engine is configured to identify the travel lanes of the road the vehicle is traveling.); and
when the barrier indicates closure of less than all of the plurality of forward travel lanes, control the autonomous vehicle to travel in an open travel lane of the plurality of forward travel lanes (Ma: Column 7 lines 1 -32, “In the depicted example, planner may generate and/or select trajectory 136 based at least in part on a closed lane status 134 associated with the current lane 122. Trajectory 136 may comprise instructions for actuating a drive system to cause the autonomous vehicle 102 to merge into an adjacent lane 124 (which may be associated with a same direction of travel and an open lane status 132).”. The cited passage clearly teaches controlling the vehicle to travel in an open travel lane when not all of the travel lanes are closed.).
Claim(s) 2, 9, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 11100339 B2 ("Ma") in further view of US 2023/0399021 A1 ("Zhang") in further view of 11458993 B2 ("Brown").
Regarding claim 2, Ma in view of Zhang does not teach wherein the processor is further configured to control the autonomous vehicle to stop when an alternative travel path for the autonomous vehicle is not present.
Brown, in the same field of endeavor, teaches wherein the processor is further configured to control the autonomous vehicle to stop when an alternative travel path for the autonomous vehicle is not present (Brown: Column 19 lines 42-53, “Based on comparing the sensor data 616a with the second portion of the map data 1510, the control subsystem 1400 determines whether the road 600 is closed ahead by identifying one or more objects 604 associated with a road closure 602 and that are not among the expected objects in the second portion of the map data 1510. In other words, the control subsystem 1400 determines the road 600 is closed if it detects the presence of one or more objects 604, such as a road closed ahead sign 604-1, one or more stopped vehicles 604-2, an object 604-3 used to close the road 600, e.g., a traffic cone, a traffic barrier, a traffic barrel, a traffic barricade tape, a delineator, and/or the like.”, Column 20 lines 7-21, “In one embodiment, once the control subsystem 1400 of the lead AV 1602-1 determines that the road 600 is closed, it may update the driving instructions 1518 of the lead AV 1602-1, such that the lead AV 1602-1 stops at a safe location behind the road closure 602 (and the vehicles 604-2). For example, the lead AV 1602-1 may proceed toward the road closure 602 and stop at a safe location behind the vehicles 604-2 while keeping a safe distance from them (e.g., 20 feet behind the stopped vehicle 604-2a ). In cases where there are no vehicles 604-2, the lead AV 1602-1 may stop at a safe location behind the road closure 602 ( e.g., 20 feet behind the road closure 602). In this way, if the road 600 is opened (i.e., the road closure 602 is removed, e.g., objects as a result of car crash are removed), the lead AV 1602-1 may resume its autonomous driving.”. The cited passages teach a method of stopping an autonomous vehicle when all lanes of the road the vehicle is travelling are closed.).
Ma in view of Zhang teaches a path determination system. Ma in view of Zhang does not teach wherein the processor is further configured to control the autonomous vehicle to stop when an alternative travel path for the autonomous vehicle is not present. Brown teaches wherein the processor is further configured to control the autonomous vehicle to stop when an alternative travel path for the autonomous vehicle is not present. A person of ordinary skill in the art would have had the technological capabilities required to have modified the path determination system taught in Ma in view of Zhang with wherein the processor is further configured to control the autonomous vehicle to stop when an alternative travel path for the autonomous vehicle is not present taught in Brown. Furthermore, the path determination system taught in Ma in view of Zhang is already configured to control the vehicle to follow a trajectory determined based on the presence of a road closure, so modifying the system to stop the vehicle when no alternate paths are available would not change or introduce new functionality. No inventive effort would have been required. The combination would have yielded the predictable result of a path determination system wherein the processor is further configured to control the autonomous vehicle to stop when an alternative travel path for the autonomous vehicle is not present.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have combine the path determination system taught in Ma in view of Zhang with wherein the processor is further configured to control the autonomous vehicle to stop when an alternative travel path for the autonomous vehicle is not present taught in Brown with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification because the combination would have yielded predictable results.
Regarding claim 9, Ma in view of Zhang does not teach further comprising the autonomous vehicle to stop when an alternative travel path for the autonomous vehicle is not present.
Brown, in the same field of endeavor, teaches further comprising the autonomous vehicle to stop when an alternative travel path for the autonomous vehicle is not present (Brown: Column 19 lines 42-53, “Based on comparing the sensor data 616a with the second portion of the map data 1510, the control subsystem 1400 determines whether the road 600 is closed ahead by identifying one or more objects 604 associated with a road closure 602 and that are not among the expected objects in the second portion of the map data 1510. In other words, the control subsystem 1400 determines the road 600 is closed if it detects the presence of one or more objects 604, such as a road closed ahead sign 604-1, one or more stopped vehicles 604-2, an object 604-3 used to close the road 600, e.g., a traffic cone, a traffic barrier, a traffic barrel, a traffic barricade tape, a delineator, and/or the like.”, Column 20 lines 7-21, “In one embodiment, once the control subsystem 1400 of the lead AV 1602-1 determines that the road 600 is closed, it may update the driving instructions 1518 of the lead AV 1602-1, such that the lead AV 1602-1 stops at a safe location behind the road closure 602 (and the vehicles 604-2). For example, the lead AV 1602-1 may proceed toward the road closure 602 and stop at a safe location behind the vehicles 604-2 while keeping a safe distance from them (e.g., 20 feet behind the stopped vehicle 604-2a ). In cases where there are no vehicles 604-2, the lead AV 1602-1 may stop at a safe location behind the road closure 602 ( e.g., 20 feet behind the road closure 602). In this way, if the road 600 is opened (i.e., the road closure 602 is removed, e.g., objects as a result of car crash are removed), the lead AV 1602-1 may resume its autonomous driving.”. The cited passages teach a method of stopping an autonomous vehicle when all lanes of the road the vehicle is travelling are closed.).
Ma in view of Zhang teaches a path determination method. Ma in view of Zhang does not teach further comprising the autonomous vehicle to stop when an alternative travel path for the autonomous vehicle is not present. Brown teaches further comprising the autonomous vehicle to stop when an alternative travel path for the autonomous vehicle is not present. A person of ordinary skill in the art would have had the technological capabilities required to have modified the path determination method taught in Ma in view of Zhang with further comprising the autonomous vehicle to stop when an alternative travel path for the autonomous vehicle is not present taught in Brown. Furthermore, the path determination method taught in Ma in view of Zhang is already configured to control the vehicle to follow a trajectory determined based on the presence of a road closure, so modifying the method to stop the vehicle when no alternate paths are available would not change or introduce new functionality. No inventive effort would have been required. The combination would have yielded the predictable result of a path determination method further comprising the autonomous vehicle to stop when an alternative travel path for the autonomous vehicle is not present.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have combine the path determination method taught in Ma in view of Zhang with further comprising the autonomous vehicle to stop when an alternative travel path for the autonomous vehicle is not present taught in Brown with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification because the combination would have yielded predictable results.
Regarding claim 16, Ma in view of Zhang does not teach wherein the processor is further configured to control the autonomous vehicle to stop when an alternative travel path for the autonomous vehicle is not present.
Brown, in the same field of endeavor, teaches wherein the processor is further configured to control the autonomous vehicle to stop when an alternative travel path for the autonomous vehicle is not present (Brown: Column 19 lines 42-53, “Based on comparing the sensor data 616a with the second portion of the map data 1510, the control subsystem 1400 determines whether the road 600 is closed ahead by identifying one or more objects 604 associated with a road closure 602 and that are not among the expected objects in the second portion of the map data 1510. In other words, the control subsystem 1400 determines the road 600 is closed if it detects the presence of one or more objects 604, such as a road closed ahead sign 604-1, one or more stopped vehicles 604-2, an object 604-3 used to close the road 600, e.g., a traffic cone, a traffic barrier, a traffic barrel, a traffic barricade tape, a delineator, and/or the like.”, Column 20 lines 7-21, “In one embodiment, once the control subsystem 1400 of the lead AV 1602-1 determines that the road 600 is closed, it may update the driving instructions 1518 of the lead AV 1602-1, such that the lead AV 1602-1 stops at a safe location behind the road closure 602 (and the vehicles 604-2). For example, the lead AV 1602-1 may proceed toward the road closure 602 and stop at a safe location behind the vehicles 604-2 while keeping a safe distance from them (e.g., 20 feet behind the stopped vehicle 604-2a ). In cases where there are no vehicles 604-2, the lead AV 1602-1 may stop at a safe location behind the road closure 602 ( e.g., 20 feet behind the road closure 602). In this way, if the road 600 is opened (i.e., the road closure 602 is removed, e.g., objects as a result of car crash are removed), the lead AV 1602-1 may resume its autonomous driving.”. The cited passages teach a method of stopping an autonomous vehicle when all lanes of the road the vehicle is travelling are closed.).
Ma in view of Zhang teaches an autonomous vehicle. Ma in view of Zhang does not teach wherein the processor is further configured to control the autonomous vehicle to stop when an alternative travel path for the autonomous vehicle is not present. Brown teaches wherein the processor is further configured to control the autonomous vehicle to stop when an alternative travel path for the autonomous vehicle is not present. A person of ordinary skill in the art would have had the technological capabilities required to have modified the autonomous vehicle taught in Ma in view of Zhang with wherein the processor is further configured to control the autonomous vehicle to stop when an alternative travel path for the autonomous vehicle is not present taught in Brown. Furthermore, the autonomous vehicle taught in Ma in view of Zhang is already configured to control the vehicle to follow a trajectory determined based on the presence of a road closure, so modifying the autonomous vehicle to stop the vehicle when no alternate paths are available would not change or introduce new functionality. No inventive effort would have been required. The combination would have yielded the predictable result of an autonomous vehicle wherein the processor is further configured to control the autonomous vehicle to stop when an alternative travel path for the autonomous vehicle is not present.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have combine the autonomous vehicle taught in Ma in view of Zhang with wherein the processor is further configured to control the autonomous vehicle to stop when an alternative travel path for the autonomous vehicle is not present taught in Brown with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification because the combination would have yielded predictable results.
Claim(s) 5, 7, 12, 14, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 11100339 B2 ("Ma") in view of US 2023/0399021 A1 ("Zhang") in further view of US 11738772 B1 ("Beller").
Regarding claim 5, Ma in view of Zhang does not teach wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to determine a road shoulder is adequate for forward travel of the autonomous vehicle
Beller, in the same field of endeavor, teaches wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to determine a road shoulder is adequate for forward travel of the autonomous vehicle (Beller: Column 13 lines 23-35, “In various examples, the action may include modifying a vehicle trajectory to cause the vehicle 404 to travel a second path (e.g., different path). The second path may include a vehicle path 416 that circumnavigates the object(s) 406. The second path and/or associated vehicle trajectory may result in the vehicle 404 continuing progress toward the destination while avoiding the object(s) 406 by at least a minimum safe distance. The vehicle trajectory and/or second path may be associated with a position of the vehicle 404 in the lane 418, a lane change (e.g., into an adjacent lane 426), and/or adjusting a position of the vehicle 404 at least partially outside the lane 418 (e.g., onto a shoulder, into a bike lane, or the like) to safely navigate around the object(s) 406.”, Column 13 lines 36-50, “In various examples, the action may include adjusting a position in a lane 418 to navigate around the object(s) 406. In some examples, the planning component 422 may determine whether the vehicle 404 is able to proceed around the object(s) 406 in the lane 418 (e.g., whether adjusting a position is a viable action). In such examples, the planning component 422 may determine the distance (D) between the object(s) 406 and/or the bounding box 420 associated therewith and a lane marker 424 (e.g., road marking delineating the edge of the lane, etc.). The planning component 422 may determine whether the distance (D) is equal to or greater than a width of the vehicle 404 and/or a safety buffer (e.g., minimum safe distance) from the object(s) 406. As discussed above, the minimum safe distance may be based on the classification 414 associated with the object(s) 406.”. The cited passages teach that the vehicle system is configured to determine a trajectory to avoid a detected object and determine if the trajectory allows the vehicle to proceed around the detected object. As can clearly be seen the new trajectory can allow the vehicle to enter the shoulder of the road.).
Ma in view of Zhang teaches a path determination system. Ma in view of Zhang does not teach wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to determine a road shoulder is adequate for forward travel of the autonomous vehicle. Beller teaches wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to determine a road shoulder is adequate for forward travel of the autonomous vehicle. A person of ordinary skill in the art would have had the technological capabilities required to have modified the path determination system taught in Ma in view of Zhang with wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to determine a road shoulder is adequate for forward travel of the autonomous vehicle taught in Beller. The path determination system taught in Ma in view of Zhang teaches determining a travel path based on a new lane designated by the detected objects for forward travel of the autonomous vehicle (Ma: Figures 3 and 4, Column 11 line 60 – Column 12 line 3, “FIG. 3 illustrates an aerial view of an example scenario 300 in which a group of safety objects designate a new lane that is not associated with traditional lane markings. The example scenario 300 in FIG. 3 depicts a shifted taper, although many other lane modifications exist such as, for example, a flagging taper, shoulder taper, merging taper, one-lane two-way traffic taper, and the like. In some examples, the techniques described herein may determine that all available lanes ( e.g., lanes associated with a same direction of travel as the vehicle) are closed in a scenario like example scenario 300.”, Column 12 lines 37-48, “FIG. 4 illustrates additional or alternative techniques for determining an open lane status and/or closed lane status associated with portion(s) of a roadway in addition to or instead of in association with traditional lane markings. For example, the techniques discussed herein may attempt to identify an open lane according to the techniques discussed above and according to traditional lane markings. However, if no such lane exists, such as in the example scenario 300, the autonomous vehicle may determine one or more alternative lane shapes and open/closed status(es) associated therewith based at least in part on one or more object detections and/or a state associated with a lane.”, Column 12 line 63 – Column 13 line 4, “The autonomous vehicle 102 may generate such a shape to have a contiguous boundary and such that there is at least one lane having a minimum width. The minimum width may be a distance that is greater than the threshold distance (so that the shape generated may avoid including safety objects of an opposite side of the tapered lane). In some examples, an ML model may be trained to determine such a shape ( e.g., based at least in part on a classification task, clustering task, and/or the like).”, Column 13 lines 5-16, “In some examples, the autonomous vehicle 102 may repeat this for a second set of safety objects (e.g., the leftward safety objects) associated with a second closed lane status and/or based at least in part on an open lane status. In other words, the autonomous vehicle 102 may inversely determine a shape that positively identifies an open lane, based at least in part on an open lane status and one or more safety objects. In some examples, the autonomous vehicle may detect a safety sign that symbolizes a taper, lane merge, and/or other traffic modification and may determine the alternative lane shape based at least in part on the symbols on the safety sign.”). In the cited passages, the example is provided for when the road closure objects require the vehicle to merge into a lane that is associated with the direction of oncoming traffic. In said example, the system is configured to determine the path through the new lane indicated by the road closure objects. Such a method could easily be modified to allow the vehicle to travel in the shoulder of the road based on a detected object as taught in Beller in the case that the road closure objects would require the vehicle to at least partially merge into the shoulder of the road. Such a modification would not change or introduce new functionality. No inventive effort would have been required. The combination would have yielded the predictable result of a path determination system wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to determine a road shoulder is adequate for forward travel of the autonomous vehicle.
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filling date of the claimed invention, to have combine the path determination system taught in Ma in view of Zhang with wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to determine a road shoulder is adequate for forward travel of the autonomous vehicle taught in Beller with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification because the combination would have yielded predictable results.
Regarding claim 7, Ma in view of Zhang does not teach wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to determine a candidate alternative travel path includes a paved portion satisfying a threshold width.
Beller, in the same field of endeavor, teaches wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to determine a candidate alternative travel path includes a paved portion satisfying a threshold width (Beller: Column 13 lines 23-35, “In various examples, the action may include modifying a vehicle trajectory to cause the vehicle 404 to travel a second path (e.g., different path). The second path may include a vehicle path 416 that circumnavigates the object(s) 406. The second path and/or associated vehicle trajectory may result in the vehicle 404 continuing progress toward the destination while avoiding the object(s) 406 by at least a minimum safe distance. The vehicle trajectory and/or second path may be associated with a position of the vehicle 404 in the lane 418, a lane change (e.g., into an adjacent lane 426), and/or adjusting a position of the vehicle 404 at least partially outside the lane 418 (e.g., onto a shoulder, into a bike lane, or the like) to safely navigate around the object(s) 406.”, Column 13 lines 36-50, “In various examples, the action may include adjusting a position in a lane 418 to navigate around the object(s) 406. In some examples, the planning component 422 may determine whether the vehicle 404 is able to proceed around the object(s) 406 in the lane 418 (e.g., whether adjusting a position is a viable action). In such examples, the planning component 422 may determine the distance (D) between the object(s) 406 and/or the bounding box 420 associated therewith and a lane marker 424 (e.g., road marking delineating the edge of the lane, etc.). The planning component 422 may determine whether the distance (D) is equal to or greater than a width of the vehicle 404 and/or a safety buffer (e.g., minimum safe distance) from the object(s) 406. As discussed above, the minimum safe distance may be based on the classification 414 associated with the object(s) 406.”. The cited passages teach that the vehicle system is configured to determine a trajectory to avoid a detected object and determine if the trajectory allows the vehicle to proceed around the detected object. Furthermore, the system is configured to determine if the distance from the object to a marker is larger than a width of the vehicle or a safety buffer. This clearly is a determination of if the travel path satisfies a width threshold. Additionally, the travel path of the vehicle is restricted to an adjacent lane or a shoulder/bike lane of the road, all of which are clearly paved portions.).
Ma in view of Zhang teaches a path determination system. Ma in view of Zhang does not teach wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to determine a candidate alternative travel path includes a paved portion satisfying a threshold width. Beller teaches wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to determine a candidate alternative travel path includes a paved portion satisfying a threshold width. A person of ordinary skill in the art would have had the technological capabilities required to have modified the path determination system taught in Ma in view of Zhang with wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to determine a candidate alternative travel path includes a paved portion satisfying a threshold width taught in Beller. Furthermore, the path determination system taught in Ma in view of Zhang is already configured to determine if the space between detected objects along a vehicles path meet a width threshold such that the vehicle can pass through them, so modifying the system such that it determines if the alternated travel path meets a width threshold using the method taught in Beller would not change or introduce new functionality. No inventive effort would have been required. The combination would have yielded the predictable result of a path determination system wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to determine a candidate alternative travel path includes a paved portion satisfying a threshold width.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have combine the path determination system taught in Ma in view of Zhang with wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to determine a candidate alternative travel path includes a paved portion satisfying a threshold width taught in Beller with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification because the combination would have yielded predictable results.
Regarding claim 12, Ma in view of Zhang does not teach wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to determine a road shoulder is adequate for forward travel of the autonomous vehicle
Beller, in the same field of endeavor, teaches wherein determining the alternative travel path for the autonomous vehicle comprises determining a road shoulder is adequate for forward travel of the autonomous vehicle (Beller: Column 13 lines 23-35, “In various examples, the action may include modifying a vehicle trajectory to cause the vehicle 404 to travel a second path (e.g., different path). The second path may include a vehicle path 416 that circumnavigates the object(s) 406. The second path and/or associated vehicle trajectory may result in the vehicle 404 continuing progress toward the destination while avoiding the object(s) 406 by at least a minimum safe distance. The vehicle trajectory and/or second path may be associated with a position of the vehicle 404 in the lane 418, a lane change (e.g., into an adjacent lane 426), and/or adjusting a position of the vehicle 404 at least partially outside the lane 418 (e.g., onto a shoulder, into a bike lane, or the like) to safely navigate around the object(s) 406.”, Column 13 lines 36-50, “In various examples, the action may include adjusting a position in a lane 418 to navigate around the object(s) 406. In some examples, the planning component 422 may determine whether the vehicle 404 is able to proceed around the object(s) 406 in the lane 418 (e.g., whether adjusting a position is a viable action). In such examples, the planning component 422 may determine the distance (D) between the object(s) 406 and/or the bounding box 420 associated therewith and a lane marker 424 (e.g., road marking delineating the edge of the lane, etc.). The planning component 422 may determine whether the distance (D) is equal to or greater than a width of the vehicle 404 and/or a safety buffer (e.g., minimum safe distance) from the object(s) 406. As discussed above, the minimum safe distance may be based on the classification 414 associated with the object(s) 406.”. The cited passages teach that the vehicle system is configured to determine a trajectory to avoid a detected object and determine if the trajectory allows the vehicle to proceed around the detected object. As can clearly be seen the new trajectory can allow the vehicle to enter the shoulder of the road.).
Ma in view of Zhang teaches a path determination method. Ma in view of Zhang does not teach wherein determining the alternative travel path for the autonomous vehicle comprises determining a road shoulder is adequate for forward travel of the autonomous vehicle. Beller teaches wherein determining the alternative travel path for the autonomous vehicle comprises determining a road shoulder is adequate for forward travel of the autonomous vehicle. A person of ordinary skill in the art would have had the technological capabilities required to have modified the path determination method taught in Ma in view of Zhang with wherein determining the alternative travel path for the autonomous vehicle comprises determining a road shoulder is adequate for forward travel of the autonomous vehicle taught in Beller. The path determination method taught in Ma in view of Zhang teaches determining a travel path based on a new lane designated by the detected objects for forward travel of the autonomous vehicle (Ma: Figures 3 and 4, Column 11 line 60 – Column 12 line 3, “FIG. 3 illustrates an aerial view of an example scenario 300 in which a group of safety objects designate a new lane that is not associated with traditional lane markings. The example scenario 300 in FIG. 3 depicts a shifted taper, although many other lane modifications exist such as, for example, a flagging taper, shoulder taper, merging taper, one-lane two-way traffic taper, and the like. In some examples, the techniques described herein may determine that all available lanes ( e.g., lanes associated with a same direction of travel as the vehicle) are closed in a scenario like example scenario 300.”, Column 12 lines 37-48, “FIG. 4 illustrates additional or alternative techniques for determining an open lane status and/or closed lane status associated with portion(s) of a roadway in addition to or instead of in association with traditional lane markings. For example, the techniques discussed herein may attempt to identify an open lane according to the techniques discussed above and according to traditional lane markings. However, if no such lane exists, such as in the example scenario 300, the autonomous vehicle may determine one or more alternative lane shapes and open/closed status(es) associated therewith based at least in part on one or more object detections and/or a state associated with a lane.”, Column 12 line 63 – Column 13 line 4, “The autonomous vehicle 102 may generate such a shape to have a contiguous boundary and such that there is at least one lane having a minimum width. The minimum width may be a distance that is greater than the threshold distance (so that the shape generated may avoid including safety objects of an opposite side of the tapered lane). In some examples, an ML model may be trained to determine such a shape ( e.g., based at least in part on a classification task, clustering task, and/or the like).”, Column 13 lines 5-16, “In some examples, the autonomous vehicle 102 may repeat this for a second set of safety objects (e.g., the leftward safety objects) associated with a second closed lane status and/or based at least in part on an open lane status. In other words, the autonomous vehicle 102 may inversely determine a shape that positively identifies an open lane, based at least in part on an open lane status and one or more safety objects. In some examples, the autonomous vehicle may detect a safety sign that symbolizes a taper, lane merge, and/or other traffic modification and may determine the alternative lane shape based at least in part on the symbols on the safety sign.”). In the cited passages, the example is provided for when the road closure objects require the vehicle to merge into a lane that is associated with the direction of oncoming traffic. In said example, the method is configured to determine the path through the new lane indicated by the road closure objects. Such a method could easily be modified to allow the vehicle to travel in the shoulder of the road based on a detected object as taught in Beller in the case that the road closure objects would require the vehicle to at least partially merge into the shoulder of the road. Such a modification would not change or introduce new functionality. No inventive effort would have been required. The combination would have yielded the predictable result of a path determination method wherein determining the alternative travel path for the autonomous vehicle comprises determining a road shoulder is adequate for forward travel of the autonomous vehicle.
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filling date of the claimed invention, to have combine the path determination method taught in Ma in view of Zhang with wherein determining the alternative travel path for the autonomous vehicle comprises determining a road shoulder is adequate for forward travel of the autonomous vehicle taught in Beller with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification because the combination would have yielded predictable results.
Regarding claim 14, Ma in view of Zhang does not teach wherein determining the alternative travel path for the autonomous vehicle comprises determining a candidate alternative travel path includes a paved portion satisfying a threshold width.
Beller, in the same field of endeavor, teaches wherein determining the alternative travel path for the autonomous vehicle comprises determining a candidate alternative travel path includes a paved portion satisfying a threshold width (Beller: Column 13 lines 23-35, “In various examples, the action may include modifying a vehicle trajectory to cause the vehicle 404 to travel a second path (e.g., different path). The second path may include a vehicle path 416 that circumnavigates the object(s) 406. The second path and/or associated vehicle trajectory may result in the vehicle 404 continuing progress toward the destination while avoiding the object(s) 406 by at least a minimum safe distance. The vehicle trajectory and/or second path may be associated with a position of the vehicle 404 in the lane 418, a lane change (e.g., into an adjacent lane 426), and/or adjusting a position of the vehicle 404 at least partially outside the lane 418 (e.g., onto a shoulder, into a bike lane, or the like) to safely navigate around the object(s) 406.”, Column 13 lines 36-50, “In various examples, the action may include adjusting a position in a lane 418 to navigate around the object(s) 406. In some examples, the planning component 422 may determine whether the vehicle 404 is able to proceed around the object(s) 406 in the lane 418 (e.g., whether adjusting a position is a viable action). In such examples, the planning component 422 may determine the distance (D) between the object(s) 406 and/or the bounding box 420 associated therewith and a lane marker 424 (e.g., road marking delineating the edge of the lane, etc.). The planning component 422 may determine whether the distance (D) is equal to or greater than a width of the vehicle 404 and/or a safety buffer (e.g., minimum safe distance) from the object(s) 406. As discussed above, the minimum safe distance may be based on the classification 414 associated with the object(s) 406.”. The cited passages teach that the vehicle system is configured to determine a trajectory to avoid a detected object and determine if the trajectory allows the vehicle to proceed around the detected object. Furthermore, the system is configured to determine if the distance from the object to a marker is larger than a width of the vehicle or a safety buffer. This clearly is a determination of if the travel path satisfies a width threshold. Additionally, the travel path of the vehicle is restricted to an adjacent lane or a shoulder/bike lane of the road, all of which are clearly paved portions.).
Ma in view of Zhang teaches a path determination method. Ma in view of Zhang does not teach wherein determining the alternative travel path for the autonomous vehicle comprises determining a candidate alternative travel path includes a paved portion satisfying a threshold width. Beller teaches wherein determining the alternative travel path for the autonomous vehicle comprises determining a candidate alternative travel path includes a paved portion satisfying a threshold width. A person of ordinary skill in the art would have had the technological capabilities required to have modified the path determination method taught in Ma in view of Zhang with wherein determining the alternative travel path for the autonomous vehicle comprises determining a candidate alternative travel path includes a paved portion satisfying a threshold width taught in Beller. Furthermore, the path determination method taught in Ma in view of Zhang is already configured to determine if the space between detected objects along a vehicles path meet a width threshold such that the vehicle can pass through them, so modifying the method such that it determines if the alternated travel path meets a width threshold using the method taught in Beller would not change or introduce new functionality. No inventive effort would have been required. The combination would have yielded the predictable result of a path determination method wherein determining the alternative travel path for the autonomous vehicle comprises determining a candidate alternative travel path includes a paved portion satisfying a threshold width.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have combine the path determination method taught in Ma in view of Zhang with wherein determining the alternative travel path for the autonomous vehicle comprises determining a candidate alternative travel path includes a paved portion satisfying a threshold width taught in Beller with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification because the combination would have yielded predictable results.
Regarding claim 19, Ma in view of Zhang does not teach wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to determine a road shoulder is adequate for forward travel of the autonomous vehicle
Beller, in the same field of endeavor, teaches wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to determine a road shoulder is adequate for forward travel of the autonomous vehicle (Beller: Column 13 lines 23-35, “In various examples, the action may include modifying a vehicle trajectory to cause the vehicle 404 to travel a second path (e.g., different path). The second path may include a vehicle path 416 that circumnavigates the object(s) 406. The second path and/or associated vehicle trajectory may result in the vehicle 404 continuing progress toward the destination while avoiding the object(s) 406 by at least a minimum safe distance. The vehicle trajectory and/or second path may be associated with a position of the vehicle 404 in the lane 418, a lane change (e.g., into an adjacent lane 426), and/or adjusting a position of the vehicle 404 at least partially outside the lane 418 (e.g., onto a shoulder, into a bike lane, or the like) to safely navigate around the object(s) 406.”, Column 13 lines 36-50, “In various examples, the action may include adjusting a position in a lane 418 to navigate around the object(s) 406. In some examples, the planning component 422 may determine whether the vehicle 404 is able to proceed around the object(s) 406 in the lane 418 (e.g., whether adjusting a position is a viable action). In such examples, the planning component 422 may determine the distance (D) between the object(s) 406 and/or the bounding box 420 associated therewith and a lane marker 424 (e.g., road marking delineating the edge of the lane, etc.). The planning component 422 may determine whether the distance (D) is equal to or greater than a width of the vehicle 404 and/or a safety buffer (e.g., minimum safe distance) from the object(s) 406. As discussed above, the minimum safe distance may be based on the classification 414 associated with the object(s) 406.”. The cited passages teach that the vehicle system is configured to determine a trajectory to avoid a detected object and determine if the trajectory allows the vehicle to proceed around the detected object. As can clearly be seen the new trajectory can allow the vehicle to enter the shoulder of the road.).
Ma in view of Zhang teaches an autonomous vehicle. Ma in view of Zhang does not teach wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to determine a road shoulder is adequate for forward travel of the autonomous vehicle. Beller teaches wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to determine a road shoulder is adequate for forward travel of the autonomous vehicle. A person of ordinary skill in the art would have had the technological capabilities required to have modified the autonomous vehicle taught in Ma in view of Zhang with wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to determine a road shoulder is adequate for forward travel of the autonomous vehicle taught in Beller. The autonomous vehicle taught in Ma in view of Zhang teaches determining a travel path based on a new lane designated by the detected objects for forward travel of the autonomous vehicle (Ma: Figures 3 and 4, Column 11 line 60 – Column 12 line 3, “FIG. 3 illustrates an aerial view of an example scenario 300 in which a group of safety objects designate a new lane that is not associated with traditional lane markings. The example scenario 300 in FIG. 3 depicts a shifted taper, although many other lane modifications exist such as, for example, a flagging taper, shoulder taper, merging taper, one-lane two-way traffic taper, and the like. In some examples, the techniques described herein may determine that all available lanes ( e.g., lanes associated with a same direction of travel as the vehicle) are closed in a scenario like example scenario 300.”, Column 12 lines 37-48, “FIG. 4 illustrates additional or alternative techniques for determining an open lane status and/or closed lane status associated with portion(s) of a roadway in addition to or instead of in association with traditional lane markings. For example, the techniques discussed herein may attempt to identify an open lane according to the techniques discussed above and according to traditional lane markings. However, if no such lane exists, such as in the example scenario 300, the autonomous vehicle may determine one or more alternative lane shapes and open/closed status(es) associated therewith based at least in part on one or more object detections and/or a state associated with a lane.”, Column 12 line 63 – Column 13 line 4, “The autonomous vehicle 102 may generate such a shape to have a contiguous boundary and such that there is at least one lane having a minimum width. The minimum width may be a distance that is greater than the threshold distance (so that the shape generated may avoid including safety objects of an opposite side of the tapered lane). In some examples, an ML model may be trained to determine such a shape ( e.g., based at least in part on a classification task, clustering task, and/or the like).”, Column 13 lines 5-16, “In some examples, the autonomous vehicle 102 may repeat this for a second set of safety objects (e.g., the leftward safety objects) associated with a second closed lane status and/or based at least in part on an open lane status. In other words, the autonomous vehicle 102 may inversely determine a shape that positively identifies an open lane, based at least in part on an open lane status and one or more safety objects. In some examples, the autonomous vehicle may detect a safety sign that symbolizes a taper, lane merge, and/or other traffic modification and may determine the alternative lane shape based at least in part on the symbols on the safety sign.”). In the cited passages, the example is provided for when the road closure objects require the vehicle to merge into a lane that is associated with the direction of oncoming traffic. In said example, the autonomous vehicle is configured to determine the path through the new lane indicated by the road closure objects. Such a method could easily be modified to allow the vehicle to travel in the shoulder of the road based on a detected object as taught in Beller in the case that the road closure objects would require the vehicle to at least partially merge into the shoulder of the road. Such a modification would not change or introduce new functionality. No inventive effort would have been required. The combination would have yielded the predictable result of an autonomous vehicle wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to determine a road shoulder is adequate for forward travel of the autonomous vehicle.
Therefore, it would have been obvious to a person of ordinary skill in the art, before the effective filling date of the claimed invention, to have combine the autonomous vehicle taught in Ma in view of Zhang with wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to determine a road shoulder is adequate for forward travel of the autonomous vehicle taught in Beller with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification because the combination would have yielded predictable results.
Claim(s) 6, 13, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 11100339 B2 ("Ma") in view of US 2023/0399021 A1 ("Zhang") in further view of KR 20180046705 A ("Lee") in further view of US 11738772 B1 ("Beller").
Regarding claim 6, Ma in view of Zhang does not teach wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to: identify a travel path of one or more other vehicles preceding the autonomous vehicle; and
determine the travel path of the one or more other vehicles is adequate for forward travel of the autonomous vehicle.
Lee, in the same field of endeavor, teaches wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to: identify a travel path of one or more other vehicles preceding the autonomous vehicle (Lee: ¶ 0243, “For example, the control unit 170 can determine that the vehicle running on the front side of the vehicle 100 (hereinafter referred to as the forward vehicle) avoids the obstacle and travels. In this case, the control section 170 can provide a signal to the vehicle drive apparatus 600 so that the vehicle 100 follows the preceding vehicle.”, ¶ 0262, “For example, the determination unit 172 can determine the obstacle avoidance driving of the preceding vehicle.”. The cited passages clearly teach determining the travel path of a preceding vehicle.).
Ma in view of Zhang teaches a path determination system. Ma in view of Zhang does not teach wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to: identify a travel path of one or more other vehicles preceding the autonomous vehicle. Lee teaches wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to: identify a travel path of one or more other vehicles preceding the autonomous vehicle. A person of ordinary skill in the art would have had the technological capabilities required to have modified the path determination system taught in Ma in view of Zhang with wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to: identify a travel path of one or more other vehicles preceding the autonomous vehicle taught in Lee. Furthermore, the path determination system is already configured to detect and determine objects through received sensor data, so modifying this to include another data regarding a preceding vehicle would not change or introduce new functionality. No inventive effort would have been required. The combination would have yielded the predictable result of a path determination system wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to: identify a travel path of one or more other vehicles preceding the autonomous vehicle.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have combine the path determination system taught in Ma in view of Zhang with wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to: identify a travel path of one or more other vehicles preceding the autonomous vehicle taught in Lee with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification because the combination would have yielded predictable results.
Ma in view of Zhang in further view of Lee does not teach determine the travel path of the one or more other vehicles is adequate for forward travel of the autonomous vehicle.
Beller, in the same field of endeavor, teaches determine the travel path of the one or more other vehicles is adequate for forward travel of the autonomous vehicle (Beller: Column 13 lines 36-50, “In various examples, the action may include adjusting a position in a lane 418 to navigate around the object(s) 406. In some examples, the planning component 422 may determine whether the vehicle 404 is able to proceed around the object(s) 406 in the lane 418 (e.g., whether adjusting a position is a viable action). In such examples, the planning component 422 may determine the distance (D) between the object(s) 406 and/or the bounding box 420 associated therewith and a lane marker 424 (e.g., road marking delineating the edge of the lane, etc.). The planning component 422 may determine whether the distance (D) is equal to or greater than a width of the vehicle 404 and/or a safety buffer (e.g., minimum safe distance) from the object(s) 406. As discussed above, the minimum safe distance may be based on the classification 414 associated with the object(s) 406.”. The cited passages teach that the vehicle system is configured to determine a trajectory to avoid a detected object and determine if the trajectory allows the vehicle to proceed around the detected object.).
Ma in view of Zhang in further view of Lee teaches a path determination system wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to: identify a travel path of one or more other vehicles preceding the autonomous vehicle. Ma in view of Zhang in further view of Lee does not teach determine the travel path of the one or more other vehicles is adequate for forward travel of the autonomous vehicle. Beller teaches determine the travel path of the one or more other vehicles is adequate for forward travel of the autonomous vehicle. A person of ordinary skill in the art would have had the technological capabilities required to have modified the path determination system taught in Ma in view of Zhang in further view of Lee with determine the travel path of the one or more other vehicles is adequate for forward travel of the autonomous vehicle taught in Beller. Furthermore, the path determination system taught in Ma in view of Zhang in further view of Lee is already configured to determining the trajectory of a preceding vehicle, whether the preceding vehicle has successfully navigated an object, and aspects such as size and location of the detected obstacles as well as of the detected travel lanes. Modifying the system such that it can determine if the trajectory of the preceding vehicle is adequate for the host vehicle using the methods taught in Beller would not change or introduce new functionality. No inventive effort would have been required. The combination would have yielded the predictable result of a path determination system that can determine the travel path of the one or more other vehicles is adequate for forward travel of the autonomous vehicle.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have combine the path determination system taught in Ma in view of Zhang in further view of Lee with determine the travel path of the one or more other vehicles is adequate for forward travel of the autonomous vehicle taught in Beller with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification because the combination would have yielded predictable results.
Regarding claim 13, Ma in view of Zhang does not teach wherein determining the alternative travel path for the autonomous vehicle comprises: identifying a travel path of one or more other vehicles preceding the autonomous vehicle; and
determining the travel path of the one or more other vehicles is adequate for forward travel of the autonomous vehicle.
Lee, in the same field of endeavor, teaches wherein determining the alternative travel path for the autonomous vehicle comprises: identifying a travel path of one or more other vehicles preceding the autonomous vehicle (Lee: ¶ 0243, “For example, the control unit 170 can determine that the vehicle running on the front side of the vehicle 100 (hereinafter referred to as the forward vehicle) avoids the obstacle and travels. In this case, the control section 170 can provide a signal to the vehicle drive apparatus 600 so that the vehicle 100 follows the preceding vehicle.”, ¶ 0262, “For example, the determination unit 172 can determine the obstacle avoidance driving of the preceding vehicle.”. The cited passages clearly teach determining the travel path of a preceding vehicle.).
Ma in view of Zhang teaches a path determination method. Ma in view of Zhang does not teach wherein determining the alternative travel path for the autonomous vehicle comprises: identifying a travel path of one or more other vehicles preceding the autonomous vehicle. Lee teaches wherein determining the alternative travel path for the autonomous vehicle comprises: identifying a travel path of one or more other vehicles preceding the autonomous vehicle. A person of ordinary skill in the art would have had the technological capabilities required to have modified the path determination method taught in Ma in view of Zhang with wherein determining the alternative travel path for the autonomous vehicle comprises: identifying a travel path of one or more other vehicles preceding the autonomous vehicle taught in Lee. Furthermore, the path determination method is already configured to detect and determine objects through received sensor data, so modifying this to include another data regarding a preceding vehicle would not change or introduce new functionality. No inventive effort would have been required. The combination would have yielded the predictable result of a path determination wherein determining the alternative travel path for the autonomous vehicle comprises: identifying a travel path of one or more other vehicles preceding the autonomous vehicle.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have combine the path determination method taught in Ma in view of Zhang with wherein determining the alternative travel path for the autonomous vehicle comprises: identifying a travel path of one or more other vehicles preceding the autonomous vehicle taught in Lee with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification because the combination would have yielded predictable results.
Ma in view of Zhang in further view of Lee does not teach determining the travel path of the one or more other vehicles is adequate for forward travel of the autonomous vehicle.
Beller, in the same field of endeavor, teaches determining the travel path of the one or more other vehicles is adequate for forward travel of the autonomous vehicle (Beller: Column 13 lines 36-50, “In various examples, the action may include adjusting a position in a lane 418 to navigate around the object(s) 406. In some examples, the planning component 422 may determine whether the vehicle 404 is able to proceed around the object(s) 406 in the lane 418 (e.g., whether adjusting a position is a viable action). In such examples, the planning component 422 may determine the distance (D) between the object(s) 406 and/or the bounding box 420 associated therewith and a lane marker 424 (e.g., road marking delineating the edge of the lane, etc.). The planning component 422 may determine whether the distance (D) is equal to or greater than a width of the vehicle 404 and/or a safety buffer (e.g., minimum safe distance) from the object(s) 406. As discussed above, the minimum safe distance may be based on the classification 414 associated with the object(s) 406.”. The cited passages teach that the vehicle system is configured to determine a trajectory to avoid a detected object and determine if the trajectory allows the vehicle to proceed around the detected object.).
Ma in view of Zhang in further view of Lee teaches a path determination method wherein determining the alternative travel path for the autonomous vehicle comprises: identifying a travel path of one or more other vehicles preceding the autonomous vehicle. Ma in view of Zhang in further view of Lee does not teach determining the travel path of the one or more other vehicles is adequate for forward travel of the autonomous vehicle. Beller teaches determining the travel path of the one or more other vehicles is adequate for forward travel of the autonomous vehicle. A person of ordinary skill in the art would have had the technological capabilities required to have modified the path determination method taught in Ma in view of Zhang in further view of Lee with determining the travel path of the one or more other vehicles is adequate for forward travel of the autonomous vehicle taught in Beller. Furthermore, the path determination method taught in Ma in view of Zhang in further view of Lee is already configured to determining the trajectory of a preceding vehicle, whether the preceding vehicle has successfully navigated an object, and aspects such as size and location of the detected obstacles as well as of the detected travel lanes. Modifying the method such that it can determine if the trajectory of the preceding vehicle is adequate for the host vehicle using the methods taught in Beller would not change or introduce new functionality. No inventive effort would have been required. The combination would have yielded the predictable result of a path determination method for determining the travel path of the one or more other vehicles is adequate for forward travel of the autonomous vehicle.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have combine the path determination method taught in Ma in view of Zhang in further view of Lee with determine the travel path of the one or more other vehicles is adequate for forward travel of the autonomous vehicle taught in Beller with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification because the combination would have yielded predictable results.
Regarding claim 20, Ma in view of Zhang does not teach wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to: identify a travel path of one or more other vehicles preceding the autonomous vehicle; and
determine the travel path of the one or more other vehicles is adequate for forward travel of the autonomous vehicle.
Lee, in the same field of endeavor, teaches wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to: identify a travel path of one or more other vehicles preceding the autonomous vehicle (Lee: ¶ 0243, “For example, the control unit 170 can determine that the vehicle running on the front side of the vehicle 100 (hereinafter referred to as the forward vehicle) avoids the obstacle and travels. In this case, the control section 170 can provide a signal to the vehicle drive apparatus 600 so that the vehicle 100 follows the preceding vehicle.”, ¶ 0262, “For example, the determination unit 172 can determine the obstacle avoidance driving of the preceding vehicle.”. The cited passages clearly teach determining the travel path of a preceding vehicle.).
Ma in view of Zhang teaches an autonomous vehicle. Ma in view of Zhang does not teach wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to: identify a travel path of one or more other vehicles preceding the autonomous vehicle. Lee teaches wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to: identify a travel path of one or more other vehicles preceding the autonomous vehicle. A person of ordinary skill in the art would have had the technological capabilities required to have modified the autonomous vehicle taught in Ma in view of Zhang with wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to: identify a travel path of one or more other vehicles preceding the autonomous vehicle taught in Lee. Furthermore, the autonomous vehicle is already configured to detect and determine objects through received sensor data, so modifying this to include another data regarding a preceding vehicle would not change or introduce new functionality. No inventive effort would have been required. The combination would have yielded the predictable result of an autonomous vehicle wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to: identify a travel path of one or more other vehicles preceding the autonomous vehicle.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have combine the autonomous vehicle taught in Ma in view of Zhang with wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to: identify a travel path of one or more other vehicles preceding the autonomous vehicle taught in Lee with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification because the combination would have yielded predictable results.
Ma in view of Zhang in further view of Lee does not teach determine the travel path of the one or more other vehicles is adequate for forward travel of the autonomous vehicle.
Beller, in the same field of endeavor, teaches determine the travel path of the one or more other vehicles is adequate for forward travel of the autonomous vehicle (Beller: Column 13 lines 36-50, “In various examples, the action may include adjusting a position in a lane 418 to navigate around the object(s) 406. In some examples, the planning component 422 may determine whether the vehicle 404 is able to proceed around the object(s) 406 in the lane 418 (e.g., whether adjusting a position is a viable action). In such examples, the planning component 422 may determine the distance (D) between the object(s) 406 and/or the bounding box 420 associated therewith and a lane marker 424 (e.g., road marking delineating the edge of the lane, etc.). The planning component 422 may determine whether the distance (D) is equal to or greater than a width of the vehicle 404 and/or a safety buffer (e.g., minimum safe distance) from the object(s) 406. As discussed above, the minimum safe distance may be based on the classification 414 associated with the object(s) 406.”. The cited passages teach that the vehicle system is configured to determine a trajectory to avoid a detected object and determine if the trajectory allows the vehicle to proceed around the detected object.).
Ma in view of Zhang in further view of Lee teaches an autonomous vehicle wherein to determine the alternative travel path for the autonomous vehicle, the processor is configured to: identify a travel path of one or more other vehicles preceding the autonomous vehicle. Ma in view of Zhang in further view of Lee does not teach determine the travel path of the one or more other vehicles is adequate for forward travel of the autonomous vehicle. Beller teaches determine the travel path of the one or more other vehicles is adequate for forward travel of the autonomous vehicle. A person of ordinary skill in the art would have had the technological capabilities required to have modified the autonomous vehicle taught in Ma in view of Zhang in further view of Lee with determine the travel path of the one or more other vehicles is adequate for forward travel of the autonomous vehicle taught in Beller. Furthermore, the autonomous vehicle taught in Ma in view of Zhang in further view of Lee is already configured to determining the trajectory of a preceding vehicle, whether the preceding vehicle has successfully navigated an object, and aspects such as size and location of the detected obstacles as well as of the detected travel lanes. Modifying the autonomous vehicle such that it can determine if the trajectory of the preceding vehicle is adequate for the host vehicle using the methods taught in Beller would not change or introduce new functionality. No inventive effort would have been required. The combination would have yielded the predictable result of an autonomous vehicle that can determine the travel path of the one or more other vehicles is adequate for forward travel of the autonomous vehicle.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to have combine the autonomous vehicle taught in Ma in view of Zhang in further view of Lee with determine the travel path of the one or more other vehicles is adequate for forward travel of the autonomous vehicle taught in Beller with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to make this modification because the combination would have yielded predictable results.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 8, and 15 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Noah W Stiebritz whose telephone number is (571)272-3414. The examiner can normally be reached Monday thru Friday 7-5 EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ramon Mercado can be reached at (571) 270-5744. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/N.W.S./Examiner, Art Unit 3658
/Ramon A. Mercado/Supervisory Patent Examiner, Art Unit 3658
Prosecution Insights