DETAILED ACTION
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 .
Status of Claims
Claims 1-12, & 14-21 of U.S. Application No. 18/138359 filed on 12/16/2025 have been examined.
Office Action is in response to the Applicant's amendments and remarks filed12/16/2025. Claims 1, 6, & 14 are presently amended. Claim 13 is cancelled. Claims 1-12, & 14-21 are presently pending and are presented for examination.
Response to Arguments
In regards to the previous rejection under 35 U.S.C. § 103: Applicant’s arguments with respect to the independent claim(s) have been considered but are moot because the new ground of rejection does not rely on the new combination of references applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. A new grounds of rejection is made in view of US 2021/0261159A1 (“Pazhayampallil”).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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, 6-8, 11, 14-16, 19, & 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2021/0133466A1 (“Gier”), in view of US 2023/0242145A1 (“Tsuchiya”), in view of US 2021/0261159A1 (“Pazhayampallil”), in view of US 2019/0377351A1 (“Phillips 351` ”), in view of US 2022/0276654A1 (“Lee”).
As per claim 1 Gier discloses
A system comprising (see at least Gier, para. [0062]: FIG. 5 is a block diagram illustrating an example system 500 for determining an updated drivable region and determining a target trajectory to pass an obstacle.):
one or more processors (see at least Gier, para. [0066]: The vehicle computing device(s) 504 can include processor(s) 516 and memory 518 communicatively coupled with processor(s) 516.); and
one or more non-transitory computer-readable media storing computer-executable instructions that, when executed, cause the one or more processors to perform operations comprising (see at least Gier, para. [0066]: The vehicle computing device(s) 504 can include processor(s) 516 and memory 518 communicatively coupled with processor(s) 516.):
receiving, from a sensor device associated with an autonomous vehicle, sensor data from an environment (see at least Gier, para. [0067-0068]: In at least one example, localization system 520 can include functionality to receive data from sensor system (s) 506 to determine a position and/or orientation of vehicle 502 ( e.g., one or more of an x-, y-, z-position, roll, pitch, or yaw). For example, localization system 520 can include and/or request/receive a map of an environment (e.g., from map(s) system 530) and can continuously determine a location and/or orientation of the autonomous vehicle within the map…In some instances, perception system 522 can include functionality to perform object detection, segmentation, and/or classification. In some examples, perception system 522 can provide processed sensor data that indicates a presence of an object that is proximate to vehicle 502,);
determining that the autonomous vehicle is traveling in a first lane (see at least Gier, para. [0067]: In some instances, localization system 520 can provide data to various components of vehicle 502 to determine an initial position of an autonomous vehicle for generating a trajectory for travelling in the environment.);
determining, based at least in part on the sensor data, an object proximate the autonomous vehicle is a double-parked vehicle (see at least Gier, para. [0017]: Although the object 116 is illustrated as a stalled vehicle, other types of obstacles are contemplated such as a double-parked vehicle, a parked vehicle that protrudes into the first drivable region 106, debris, signage, a construction zone, a pedestrian, a road defect, and the like. & para. [0047]: Although not explicitly pictured, it can be understood that the vehicle 104 can also detect that the object 116 is other types of objects such as a double-parked vehicle or other types of obstacles such as a road defect, a construction zone, and the like.);
determining a first region surrounding the object, the first region including a first region cost (see at least Gier, Fig. 2 & para. [0028]: In some instances, a region cost can refer to a cost associated with a drivable region. For example, a first region cost can be associated with the first drivable region 106 and a second region cost can be associated with the second drivable region 108.);
determining a second region adjacent the first region, the second region including a second region cost that is different than the first region cost (see at least Gier, Fig. 2 & para. [0028]: In some instances, a region cost can refer to a cost associated with a drivable region. For example, a first region cost can be associated with the first drivable region 106 and a second region cost can be associated with the second drivable region 108.);
generating, based at least in part on the first region and the second region, a trajectory associated with the autonomous vehicle (see at least Gier, para. [0037]: Based at least in part on the updated drivable region 214, the vehicle 104 can determine a candidate trajectory 216 associated with the partial lane expansion action. The candidate trajectory 216 can cause the vehicle 104 to partially traverse into the second driving lane 112 which can allow the vehicle 104 to safely pass the object 116.);
generating a first cost associated with the trajectory and a second cost associated with the trajectory (see at least Gier, para. [0101]: At operation 610, the computing device can determine a first action associated with a first cost and a second action associated with a second cost. By way of example and without limitation, the first action can be a stay in lane action and the second action can be an oncoming lane action. The first action can be associated with a first cost and the second action can be associated with a second cost where the first cost and the second cost can include costs such as a reference cost, an obstacle cost, a lateral cost, a longitudinal cost, a region cost, a width cost, an indicator cost, an action switch cost, an action cost, a utilization cost, and the like.);
controlling the autonomous vehicle based at least in part on the trajectory (see at least Gier, para. [0028]: In some instances, a region cost can refer to a cost associated with a drivable region. For example, a first region cost can be associated with the first drivable region 106 and a second region cost can be associated with the second drivable region 108. By way of example and without limitation, the first region cost can be lower than the second region cost based on a direction of travel of the vehicle and the direction of travel associated with the first drivable region 106 and/or the second drivable region 108. As can be understood, when the direction of travel of the vehicle 104 is the same as the direction of travel of the first drivable region 106 and is different from the direction of travel of the second drivable region 108, the first region cost can be lower than the second region cost.).
However Gier does not explicitly disclose
determining a dilated representation of the object;
determining, based at least in part on a capable acceleration of the object, the dilated representation of the object, and a current velocity of the autonomous vehicle, a first region surrounding the object,
the first region including a first weight;
the second region including a second weight that is different than the first weight;
modifying, based at least in part on the trajectory passing through the first region, the first cost with the first weight resulting in a modified first cost that is increased from the first cost;
modifying, based at least in part on the trajectory passing through the second region, the second cost with the second weight resulting in a modified second cost that is decreased from the second cost.
Tsuchiya teaches
determining a dilated representation of the object (see at least Tsuchiya, Fig. 4 & para. [0070-0072]: The risk area deriver 138 may adjust the risk area in accordance with the type of physical object. For example, when the physical object is the pedestrian OBa and when the physical object is the two-wheeled vehicle OBb, even if their positions, moving directions, and speeds (speeds Va and Vb shown in FIG. 4) during recognition are the same, their movement amounts may be significantly different for a subsequent prescribed time period. The pedestrian OBa and the two-wheeled vehicle OBb are different in size of the physical object itself from the four-wheeled vehicle OBc. Therefore, the risk area deriver 138 derives the risk area in accordance with the type of physical object. );
determining, based at least in part on an acceleration of the object the dilated representation of the object and a current velocity of the autonomous vehicle, a first region surrounding the object (see at least Tsuchiya, Fig. 4 & para. [0070-0072]: The risk area deriver 138 may adjust the risk area according to the speed or the moving direction of the physical object. For example, the risk area deriver 138 may derive a risk area based on the risk potential by increasing the risk potential as an absolute velocity or absolute acceleration of the physical object increases. Instead of (or in addition to) the absolute velocity or the absolute acceleration of the physical object, the risk potential may be appropriately decided in accordance with a relative velocity and relative acceleration between the host vehicle M and the physical object, time to collision (TTC), a predicted contact position, or the like. The risk potential may be adjusted in accordance with a surrounding situation such as a road shape, a degree of congestion, weather, or time of day.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gier to incorporate the teaching of the determining a dilated representation of the object; determining, based at least in part on the dilated representation of the object and a current velocity of the autonomous vehicle, a first region surrounding the object of Tsuchiya, with a reasonable expectation of success, in order for the accuracy of the position, size, and type of the physical object to be improved and more accurate physical object recognition (see at least Tsuchiya, para. [0091]).
Pazhayampallil teaches
determining, based at least in part on a capable acceleration of the object, the dilated representation of the object, a first region surrounding the object (see at least Pazhayampallil, para. [0056]: The autonomous vehicle can then implement methods and techniques described above: to recalculate a critical time of the autonomous vehicle based on the autonomous vehicle's speed during this second scan cycle; and to recalculate a future state boundary of the object from the current time to this revised critical time based on the true (absolute or relative) velocity of the object (rather than the worst-case speed of a generic object), the angular velocity of the object, and the maximum possible acceleration of a generic object—limited by the maximum possible speed of a generic object—from the current time to the revised critical time. & para. [0064]: The autonomous vehicle can repeat this process for the subsequent scan cycle, including: further revising the maximum possible azimuthal speed of the virtual object—along the azimuthal direction relative to the autonomous vehicle—based on the length of the first object and the time interval over the set of scan images in which the autonomous vehicle detected the first object; recalculating maximum possible speeds and accelerations of the virtual object in various directions based on this maximum possible azimuthal speed of the virtual object; and refining the virtual future state boundary of the virtual object based on these maximum possible speeds, maximum possible accelerations, and the maximum possible azimuthal speed of the virtual object.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gier to incorporate the teaching of determining, based at least in part on a capable acceleration of the object, the dilated representation of the object, a first region surrounding the object of Pazhayampallil, with a reasonable expectation of success, in order to improve the autonomous vehicle certainty of the motion of the object in the future (see at least Pazhayampallil, para. [0177]).
Phillips 351` teaches
the first region including a first weight (see at least Phillips 351`, para. [0062]: In some implementations, different scoring factors can be associated with the different first and second spatial regions such that trajectory paths determined relative to such spatial regions can be costed accordingly in a customizable fashion relative to desired action of the autonomous vehicle relative to the spatial regions of the gridlock constraint.);
the second region including a second weight that is different than the first weight (see at least Phillips 351`, para. [0062]: In some implementations, different scoring factors can be associated with the different first and second spatial regions such that trajectory paths determined relative to such spatial regions can be costed accordingly in a customizable fashion relative to desired action of the autonomous vehicle relative to the spatial regions of the gridlock constraint.);
modifying, based at least in part on the trajectory passing through the first region, the first cost with the first weight resulting in a modified first cost that is increased from the first cost (see at least Phillips 351`, para. [0065]: With more particular reference to determining a low-cost trajectory path, the score generated for each candidate trajectory path can include one or more scoring factors, including but not limited to costs, discounts and/or rewards associated with aspects of a candidate trajectory path for use in evaluation of a cost function or other scoring equation. Example scoring factors can include, for example, a dynamics cost for given dynamics (e.g., jerk, acceleration) associated with the candidate trajectory path, a buffer cost associated with proximity of a candidate trajectory path to one or more constraints and/or buffer zones within the multi-dimensional space, a constraint violation cost associated with violating one or more constraints and/or buffer zones, a reward or discount for one or more achieved performance objectives (e.g., a distance traveled reward for moving forward as opposed to not moving), a blind spot cost associated with a candidate trajectory path that involves spending time in a blind spot of other actors (e.g., other vehicles). para. [0117]: According to an aspect of the present disclosure, the total cost can be based at least in part on one or more cost functions 304. In one example implementation, the total cost equals the sum of all costs minus the sum of all rewards and the optimization planner attempts to minimize the total cost. The cost functions 304 can be evaluated by a penalty/reward generator 302. & para. [0120]: Thus, if a candidate motion plan approaches a proximate object of interest, the first cost increases, thereby discouraging (e.g., through increased cost penalization) the autonomous vehicle from selecting motion plans that come undesirably close to the object.);
modifying, based at least in part on the trajectory passing through the second region, the second cost with the second weight resulting in a modified second cost (see at least Phillips 351`, para. [0065]: With more particular reference to determining a low-cost trajectory path, the score generated for each candidate trajectory path can include one or more scoring factors, including but not limited to costs, discounts and/or rewards associated with aspects of a candidate trajectory path for use in evaluation of a cost function or other scoring equation. Example scoring factors can include, for example, a dynamics cost for given dynamics (e.g., jerk, acceleration) associated with the candidate trajectory path, a buffer cost associated with proximity of a candidate trajectory path to one or more constraints and/or buffer zones within the multi-dimensional space, a constraint violation cost associated with violating one or more constraints and/or buffer zones, a reward or discount for one or more achieved performance objectives (e.g., a distance traveled reward for moving forward as opposed to not moving), a blind spot cost associated with a candidate trajectory path that involves spending time in a blind spot of other actors (e.g., other vehicles). para. [0117]: According to an aspect of the present disclosure, the total cost can be based at least in part on one or more cost functions 304. In one example implementation, the total cost equals the sum of all costs minus the sum of all rewards and the optimization planner attempts to minimize the total cost. The cost functions 304 can be evaluated by a penalty/reward generator 302.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gier to incorporate the teaching of the first region including a first weight; the second region including a second weight that is different than the first weight of Phillips 351`, with a reasonable expectation of success, in order to identify an appropriate motion path through such surrounding environment (see at least Phillips 351`, para. [0003]).
Lee teaches
modifying, based at least in part on the trajectory passing through the first region, the first cost with the first weight resulting in a modified first cost that is increased from the first cost (see at least Lee, para. [0050]: In addition, the cost allocator 120 may allocate a relatively high cost as an area resulting from division of the space is closer to the obstacle, and a relatively low cost as an area resulting from division of the space is further away from the obstacle, and then generate a cost map by storing a cost value thereof along with map information and obstacle information. Accordingly, the determination as to which area of areas is to be included in a path and which area of areas is to be avoided may be achieved merely by comparing the costs allocated to the areas with each other. para. [0054]: In addition, the cost map generation module 100 may differently allocate a cost to areas in such a way that the cost decreases linearly as the distance increases from the area occupied by the obstacle as shown in FIG. 2. Of course, it is also possible to allocate a cost such that the cost decreases rapidly non-linearly as the distance increases. & para. [0084]: In addition, as shown in FIG. 10, when the driving condition requested from the mobile robot is to preferentially drive in an area in which a safe distance from an obstacle is secured, the cost function factor determiner 310 may determine, as a cost function factor, safety of the cost map or congestion of the cost map, and the path selector 320 may select a path with the minimum cost as the optimal path by calculating a cumulative or average cost value in a case where the mobile robot drives along each of paths by referring to a cost value matched to each area on the cost map.);
modifying, based at least in part on the trajectory passing through the second region, the second cost with the second weight resulting in a modified second cost that is decreased from the second cost (see at least Lee, para. [0050]: In addition, the cost allocator 120 may allocate a relatively high cost as an area resulting from division of the space is closer to the obstacle, and a relatively low cost as an area resulting from division of the space is further away from the obstacle, and then generate a cost map by storing a cost value thereof along with map information and obstacle information. Accordingly, the determination as to which area of areas is to be included in a path and which area of areas is to be avoided may be achieved merely by comparing the costs allocated to the areas with each other. para. [0054]: In addition, the cost map generation module 100 may differently allocate a cost to areas in such a way that the cost decreases linearly as the distance increases from the area occupied by the obstacle as shown in FIG. 2. Of course, it is also possible to allocate a cost such that the cost decreases rapidly non-linearly as the distance increases. & para. [0084]: In addition, as shown in FIG. 10, when the driving condition requested from the mobile robot is to preferentially drive in an area in which a safe distance from an obstacle is secured, the cost function factor determiner 310 may determine, as a cost function factor, safety of the cost map or congestion of the cost map, and the path selector 320 may select a path with the minimum cost as the optimal path by calculating a cumulative or average cost value in a case where the mobile robot drives along each of paths by referring to a cost value matched to each area on the cost map.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gier to incorporate the teaching of controlling the autonomous vehicle based at least in part on the trajectory, wherein a first cost associated with the trajectory is increased based at least in part on the trajectory passing through the first region, and a second cost associated with the trajectory is decreased based at least in part on the trajectory passing through the second region of Lee, with a reasonable expectation of success, in order for improving the convenience of cost allocation for each area (see at least Lee, para. [0048]).
As per claim 2 Gier discloses
wherein the first cost is associated with a progress cost determined based at least in part on whether the autonomous vehicle, while traversing the trajectory, progressed towards a destination. (see at least Gier, para. [0023]: In general, the one or more costs can include, but is not limited to a reference cost, an obstacle cost, a lateral cost, a longitudinal cost, a region cost, a width cost, an indicator cost, an action switch cost, an action cost, a utilization cost, and the like.).
As per claim 3 Gier discloses
wherein a first size of the first region is determined based at least in part on at least one of:
a type of the object,
a second size of the object,
a kinematic of the autonomous vehicle,
a velocity of the autonomous vehicle,
semantic information of an environment proximate the autonomous vehicle (see at least Gier, para. [0015]: The vehicle can be configured to use the map data to determine the first drivable region 106 and the second drivable region 108. For example, the map data can indicate that the first drivable region is associated with a first driving lane 110 and that the second drivable region is associated with a second driving lane 112. Additionally, the map data can indicate that the first drivable region 106 is associated with a first direction of travel and that the second drivable region 108 is associated with a second direction of travel that is different from the first direction of travel. In some instances, the map data can include road marker data (e.g., single yellow markers, double yellow markers, single white markers, double white markers, solid markers, broken markers, and the like).),
a difference of velocity between the autonomous vehicle and the object, or an orientation of the object.
As per claim 6 Gier discloses
One or more non-transitory computer-readable media storing instructions executable by one or more processors, wherein the instructions, when executed, cause the one or more processors to perform operations comprising (see at least Gier, para. [0093]: Memory 518 and 546 are examples of non-transitory computer-readable media. The memories 518 and 546 can store an operating system and one or more software applications, instructions, programs, and/or data to implement the methods described herein and the functions attributed to the various systems.):
determining a blocking object proximate travel of a vehicle (see at least Gier, para. [0017]: Although the object 116 is illustrated as a stalled vehicle, other types of obstacles are contemplated such as a double-parked vehicle, a parked vehicle that protrudes into the first drivable region 106, debris, signage, a construction zone, a pedestrian, a road defect, and the like. & para. [0047]: Although not explicitly pictured, it can be understood that the vehicle 104 can also detect that the object 116 is other types of objects such as a double-parked vehicle or other types of obstacles such as a road defect, a construction zone, and the like.);
one or more of determining or receiving a first region proximate the blocking object, the first region indicating a first region cost associated with controlling the vehicle within the first region (see at least Gier, Fig. 2 & para. [0015]: At operation 102, a vehicle 104 can determine, based at least in part on map data, a first drivable region 106 and a second drivable region 108. para. [0028]: In some instances, a region cost can refer to a cost associated with a drivable region. For example, a first region cost can be associated with the first drivable region 106 and a second region cost can be associated with the second drivable region 108. & para. [0037]: Based at least in part on the updated drivable region 214, the vehicle 104 can determine a candidate trajectory 216 associated with the partial lane expansion action. The candidate trajectory 216 can cause the vehicle 104 to partially traverse into the second driving lane 112 which can allow the vehicle 104 to safely pass the object 116.);
one or more of determining or receiving a second region adjacent the first region, the second region indicating a second region cost associated with controlling the vehicle within the second region (see at least Gier, Fig. 2 & para. [0015]: At operation 102, a vehicle 104 can determine, based at least in part on map data, a first drivable region 106 and a second drivable region 108. & para. [0028]: In some instances, a region cost can refer to a cost associated with a drivable region. For example, a first region cost can be associated with the first drivable region 106 and a second region cost can be associated with the second drivable region 108. By way of example and without limitation, the first region cost can be lower than the second region cost based on a direction of travel of the vehicle and the direction of travel associated with the first drivable region 106 and/or the second drivable region 108. ); and
determining, based at least in part on the first region and the second region, a cost associated with a trajectory for controlling the vehicle proximate the blocking object (see at least Gier, para. [0028]: In some instances, a region cost can refer to a cost associated with a drivable region. For example, a first region cost can be associated with the first drivable region 106 and a second region cost can be associated with the second drivable region 108. By way of example and without limitation, the first region cost can be lower than the second region cost based on a direction of travel of the vehicle and the direction of travel associated with the first drivable region 106 and/or the second drivable region 108. As can be understood, when the direction of travel of the vehicle 104 is the same as the direction of travel of the first drivable region 106 and is different from the direction of travel of the second drivable region 108, the first region cost can be lower than the second region cost. & para. [0037]: Based at least in part on the updated drivable region 214, the vehicle 104 can determine a candidate trajectory 216 associated with the partial lane expansion action. The candidate trajectory 216 can cause the vehicle 104 to partially traverse into the second driving lane 112 which can allow the vehicle 104 to safely pass the object 116.).
However Gier does not explicitly disclose
determining, based at least in part on a distance between the vehicle and the blocking object, a dilated representation of the blocking object;
one or more of determining or receiving, based at least in part on the dilated representation of the blocking object, a capable acceleration of the blocking object, and a current velocity of the vehicle, a first region proximate the blocking object,
the first region including a first weight to increase a first cost associated with controlling the vehicle within the first region;
the second region including a second weight to decrease a second cost associated with controlling the vehicle within the second region;
determining, based at least in part on the first weight and the first cost, a first modified cost;
determining, based at least in part on the second weight and the second cost, a second modified cost;
determining, based at least in part on the first modified cost and the second modified cost, a cost associated with a trajectory for controlling the vehicle proximate the blocking object.
Tsuchiya teaches
determining, based at least in part on a distance between the vehicle and the blocking object, a dilated representation of the blocking object (see at least Tsuchiya, Fig. 4 & para. [0070-0072]: The risk area deriver 138 may adjust the risk area in accordance with the type of physical object. For example, when the physical object is the pedestrian OBa and when the physical object is the two-wheeled vehicle OBb, even if their positions, moving directions, and speeds (speeds Va and Vb shown in FIG. 4) during recognition are the same, their movement amounts may be significantly different for a subsequent prescribed time period. The pedestrian OBa and the two-wheeled vehicle OBb are different in size of the physical object itself from the four-wheeled vehicle OBc. Therefore, the risk area deriver 138 derives the risk area in accordance with the type of physical object. & para. [0069]: The risk area deriver 138 derives a risk area where the risk potential increases as a distance from the physical object decreases and the risk potential decreases as the distance from the physical object increases. The risk area deriver 138 may make an adjustment so that the risk potential increases as a distance between the host vehicle M and the physical object decreases (in other words, the risk potential decreases as the distance between the host vehicle M and the physical object increases).);
one or more of determining or receiving, based at least in part on the dilated representation of the blocking object, an acceleration of the blocking object, and a current velocity of the vehicle, a first region proximate the blocking object (see at least Tsuchiya, Fig. 4 & para. [0070-0072]: The risk area deriver 138 may adjust the risk area according to the speed or the moving direction of the physical object. For example, the risk area deriver 138 may derive a risk area based on the risk potential by increasing the risk potential as an absolute velocity or absolute acceleration of the physical object increases. Instead of (or in addition to) the absolute velocity or the absolute acceleration of the physical object, the risk potential may be appropriately decided in accordance with a relative velocity and relative acceleration between the host vehicle M and the physical object, time to collision (TTC), a predicted contact position, or the like. The risk potential may be adjusted in accordance with a surrounding situation such as a road shape, a degree of congestion, weather, or time of day.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gier to incorporate the teaching of determining, based at least in part on a distance between the vehicle and the blocking object, a dilated representation of the blocking object, one or more of determining or receiving, based at least in part on the dilated representation of the blocking object and a current velocity of the vehicle, a first region proximate the blocking object, of Tsuchiya, with a reasonable expectation of success, in order for the accuracy of the position, size, and type of the physical object to be improved and more accurate physical object recognition (see at least Tsuchiya, para. [0091]).
Pazhayampallil teaches
one or more of determining or receiving, based at least in part on the dilated representation of the blocking object, a capable acceleration of the blocking object, a first region proximate the blocking object (see at least Pazhayampallil, para. [0056]: The autonomous vehicle can then implement methods and techniques described above: to recalculate a critical time of the autonomous vehicle based on the autonomous vehicle's speed during this second scan cycle; and to recalculate a future state boundary of the object from the current time to this revised critical time based on the true (absolute or relative) velocity of the object (rather than the worst-case speed of a generic object), the angular velocity of the object, and the maximum possible acceleration of a generic object—limited by the maximum possible speed of a generic object—from the current time to the revised critical time. & para. [0064]: The autonomous vehicle can repeat this process for the subsequent scan cycle, including: further revising the maximum possible azimuthal speed of the virtual object—along the azimuthal direction relative to the autonomous vehicle—based on the length of the first object and the time interval over the set of scan images in which the autonomous vehicle detected the first object; recalculating maximum possible speeds and accelerations of the virtual object in various directions based on this maximum possible azimuthal speed of the virtual object; and refining the virtual future state boundary of the virtual object based on these maximum possible speeds, maximum possible accelerations, and the maximum possible azimuthal speed of the virtual object.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gier to incorporate the teaching of one or more of determining or receiving, based at least in part on the dilated representation of the blocking object, a capable acceleration of the blocking object, a first region proximate the blocking object of Pazhayampallil, with a reasonable expectation of success, in order to improve the autonomous vehicle certainty of the motion of the object in the future (see at least Pazhayampallil, para. [0177]).
Phillips 351` teaches
the first region including a first weight to increase a first cost associated with controlling the vehicle within the first region (see at least Phillips 351`, para. [0062]: In some implementations, different scoring factors can be associated with the different first and second spatial regions such that trajectory paths determined relative to such spatial regions can be costed accordingly in a customizable fashion relative to desired action of the autonomous vehicle relative to the spatial regions of the gridlock constraint. Para. [0120]: Thus, if a candidate motion plan approaches a proximate object of interest, the first cost increases, thereby discouraging (e.g., through increased cost penalization) the autonomous vehicle from selecting motion plans that come undesirably close to the object.);
the second region including a second weight associated with controlling the vehicle within the second region (see at least Phillips 351`, para. [0062]: In some implementations, different scoring factors can be associated with the different first and second spatial regions such that trajectory paths determined relative to such spatial regions can be costed accordingly in a customizable fashion relative to desired action of the autonomous vehicle relative to the spatial regions of the gridlock constraint.);
determining, based at least in part on the first weight and the first cost, a first modified cost (see at least Phillips 351`, para. [0065]: With more particular reference to determining a low-cost trajectory path, the score generated for each candidate trajectory path can include one or more scoring factors, including but not limited to costs, discounts and/or rewards associated with aspects of a candidate trajectory path for use in evaluation of a cost function or other scoring equation. para. [0117]: According to an aspect of the present disclosure, the total cost can be based at least in part on one or more cost functions 304. In one example implementation, the total cost equals the sum of all costs minus the sum of all rewards and the optimization planner attempts to minimize the total cost. The cost functions 304 can be evaluated by a penalty/reward generator 302.);
determining, based at least in part on the second weight and the second cost, a second modified cost (see at least Phillips 351`, para. [0065]: With more particular reference to determining a low-cost trajectory path, the score generated for each candidate trajectory path can include one or more scoring factors, including but not limited to costs, discounts and/or rewards associated with aspects of a candidate trajectory path for use in evaluation of a cost function or other scoring equation. para. [0117]: According to an aspect of the present disclosure, the total cost can be based at least in part on one or more cost functions 304. In one example implementation, the total cost equals the sum of all costs minus the sum of all rewards and the optimization planner attempts to minimize the total cost. The cost functions 304 can be evaluated by a penalty/reward generator 302.);
determining, based at least in part on the first modified cost and the second modified cost, a cost associated with a trajectory for controlling the vehicle (see at least Phillips 351`, para. [0065]: With more particular reference to determining a low-cost trajectory path, the score generated for each candidate trajectory path can include one or more scoring factors, including but not limited to costs, discounts and/or rewards associated with aspects of a candidate trajectory path for use in evaluation of a cost function or other scoring equation. para. [0117]: According to an aspect of the present disclosure, the total cost can be based at least in part on one or more cost functions 304. In one example implementation, the total cost equals the sum of all costs minus the sum of all rewards and the optimization planner attempts to minimize the total cost. The cost functions 304 can be evaluated by a penalty/reward generator 302.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gier to incorporate the teaching of the first region including a first weight to increase a first cost associated with controlling the vehicle within the first region, determining, based at least in part on the second weight and the second cost, a second modified cost; determining, based at least in part on the first modified cost and the second modified cost, a cost associated with a trajectory for controlling the vehicle of Phillips 351`, with a reasonable expectation of success, in order to identify an appropriate motion path through such surrounding environment (see at least Phillips 351`, para. [0003]).
Lee teaches
one or more of determining or receiving a first region proximate the blocking object (see at least Lee, Figs. 2-8),
the first region including a weight to increase a first cost associated with controlling the vehicle within the first region (see at least Lee, para. [0050]: In addition, the cost allocator 120 may allocate a relatively high cost as an area resulting from division of the space is closer to the obstacle, and a relatively low cost as an area resulting from division of the space is further away from the obstacle, and then generate a cost map by storing a cost value thereof along with map information and obstacle information.);
one or more of determining or receiving a second region adjacent the first region (see at least Lee, Figs. 2-8),
the second region including a weight to decrease a second cost associated with controlling the vehicle within the second region (see at least Lee, para. [0050]: In addition, the cost allocator 120 may allocate a relatively high cost as an area resulting from division of the space is closer to the obstacle, and a relatively low cost as an area resulting from division of the space is further away from the obstacle, and then generate a cost map by storing a cost value thereof along with map information and obstacle information.);
determining, based at least in part on the weight and the first cost, a first modified cost (see at least Lee, para. [0054]: In addition, the cost map generation module 100 may differently allocate a cost to areas in such a way that the cost decreases linearly as the distance increases from the area occupied by the obstacle as shown in FIG. 2. Of course, it is also possible to allocate a cost such that the cost decreases rapidly non-linearly as the distance increases. & para. [0084]: In addition, as shown in FIG. 10, when the driving condition requested from the mobile robot is to preferentially drive in an area in which a safe distance from an obstacle is secured, the cost function factor determiner 310 may determine, as a cost function factor, safety of the cost map or congestion of the cost map, and the path selector 320 may select a path with the minimum cost as the optimal path by calculating a cumulative or average cost value in a case where the mobile robot drives along each of paths by referring to a cost value matched to each area on the cost map.);
determining, based at least in part on the weight and the second cost, a second modified cost (see at least Lee, para. [0054]: In addition, the cost map generation module 100 may differently allocate a cost to areas in such a way that the cost decreases linearly as the distance increases from the area occupied by the obstacle as shown in FIG. 2. Of course, it is also possible to allocate a cost such that the cost decreases rapidly non-linearly as the distance increases. & para. [0084]: In addition, as shown in FIG. 10, when the driving condition requested from the mobile robot is to preferentially drive in an area in which a safe distance from an obstacle is secured, the cost function factor determiner 310 may determine, as a cost function factor, safety of the cost map or congestion of the cost map, and the path selector 320 may select a path with the minimum cost as the optimal path by calculating a cumulative or average cost value in a case where the mobile robot drives along each of paths by referring to a cost value matched to each area on the cost map.);
determining, based at least in part on the first modified cost and the second modified cost, a cost associated with a trajectory for controlling the vehicle proximate the blocking object (see at least Lee, para. [0050]: In addition, the cost allocator 120 may allocate a relatively high cost as an area resulting from division of the space is closer to the obstacle, and a relatively low cost as an area resulting from division of the space is further away from the obstacle, and then generate a cost map by storing a cost value thereof along with map information and obstacle information. para. [0054]: In addition, the cost map generation module 100 may differently allocate a cost to areas in such a way that the cost decreases linearly as the distance increases from the area occupied by the obstacle as shown in FIG. 2. Of course, it is also possible to allocate a cost such that the cost decreases rapidly non-linearly as the distance increases. & para. [0084]: In addition, as shown in FIG. 10, when the driving condition requested from the mobile robot is to preferentially drive in an area in which a safe distance from an obstacle is secured, the cost function factor determiner 310 may determine, as a cost function factor, safety of the cost map or congestion of the cost map, and the path selector 320 may select a path with the minimum cost as the optimal path by calculating a cumulative or average cost value in a case where the mobile robot drives along each of paths by referring to a cost value matched to each area on the cost map.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gier to incorporate the teaching of the first region including a weight to increase a first cost associated with controlling the vehicle within the first region, the second region including a weight to decrease a second cost associated with controlling the vehicle within the second region, determining, based at least in part on the weight and the first cost, a first modified cost, determining, based at least in part on the weight and the second cost, a second modified cost, determining, based at least in part on the first modified cost and the second modified cost, a cost associated with a trajectory for controlling the vehicle proximate the blocking object, of Lee, with a reasonable expectation of success, in order for improving the convenience of cost allocation for each area (see at least Lee, para. [0048]).
As per claim 7 Gier discloses
wherein the first cost is associated with progress to a destination along the trajectory (see at least Gier, para. [0023]: In general, the one or more costs can include, but is not limited to a reference cost, an obstacle cost, a lateral cost, a longitudinal cost, a region cost, a width cost, an indicator cost, an action switch cost, an action cost, a utilization cost, and the like.).
As per claim 8 Gier discloses
wherein a first size of the first region is determined based at least in part on at least one of:
a type of the blocking object,
a size of the blocking object,
a kinematic of the vehicle,
semantic information of an environment proximate the vehicle (see at least Gier, para. [0015]: The vehicle can be configured to use the map data to determine the first drivable region 106 and the second drivable region 108. For example, the map data can indicate that the first drivable region is associated with a first driving lane 110 and that the second drivable region is associated with a second driving lane 112. Additionally, the map data can indicate that the first drivable region 106 is associated with a first direction of travel and that the second drivable region 108 is associated with a second direction of travel that is different from the first direction of travel. In some instances, the map data can include road marker data (e.g., single yellow markers, double yellow markers, single white markers, double white markers, solid markers, broken markers, and the like).),
a velocity of the vehicle,
a difference of speed between the vehicle and the blocking object, or an orientation of the blocking object.
As per claim 11 Gier discloses
wherein controlling the vehicle in accordance with the trajectory causes the vehicle to move at least partially into an oncoming lane of traffic (see at least Gier, para. [0037]: Based at least in part on the updated drivable region 214, the vehicle 104 can determine a candidate trajectory 216 associated with the partial lane expansion action. The candidate trajectory 216 can cause the vehicle 104 to partially traverse into the second driving lane 112 which can allow the vehicle 104 to safely pass the object 116.).
As per claim 14 Gier discloses
A method comprising:
determining a blocking object proximate travel of a vehicle (see at least Gier, para. [0017]: Although the object 116 is illustrated as a stalled vehicle, other types of obstacles are contemplated such as a double-parked vehicle, a parked vehicle that protrudes into the first drivable region 106, debris, signage, a construction zone, a pedestrian, a road defect, and the like. & para. [0047]: Although not explicitly pictured, it can be understood that the vehicle 104 can also detect that the object 116 is other types of objects such as a double-parked vehicle or other types of obstacles such as a road defect, a construction zone, and the like.);
one or more of determining or receiving a first region proximate the blocking object, the first region indicating a first region cost associated with controlling the vehicle within the first region (see at least Gier, Fig. 2 & para. [0015]: At operation 102, a vehicle 104 can determine, based at least in part on map data, a first drivable region 106 and a second drivable region 108. para. [0028]: In some instances, a region cost can refer to a cost associated with a drivable region. For example, a first region cost can be associated with the first drivable region 106 and a second region cost can be associated with the second drivable region 108. & para. [0037]: Based at least in part on the updated drivable region 214, the vehicle 104 can determine a candidate trajectory 216 associated with the partial lane expansion action. The candidate trajectory 216 can cause the vehicle 104 to partially traverse into the second driving lane 112 which can allow the vehicle 104 to safely pass the object 116.);
one or more of determining or receiving a second region adjacent the first region, the second region indicating a second region cost associated with controlling the vehicle within the second region (see at least Gier, Fig. 2 & para. [0015]: At operation 102, a vehicle 104 can determine, based at least in part on map data, a first drivable region 106 and a second drivable region 108. & para. [0028]: In some instances, a region cost can refer to a cost associated with a drivable region. For example, a first region cost can be associated with the first drivable region 106 and a second region cost can be associated with the second drivable region 108. By way of example and without limitation, the first region cost can be lower than the second region cost based on a direction of travel of the vehicle and the direction of travel associated with the first drivable region 106 and/or the second drivable region 108. ); and
determining, based at least in part on the first region and the second region, a cost associated with a trajectory for controlling the vehicle proximate the blocking object (see at least Gier, para. [0028]: In some instances, a region cost can refer to a cost associated with a drivable region. For example, a first region cost can be associated with the first drivable region 106 and a second region cost can be associated with the second drivable region 108. By way of example and without limitation, the first region cost can be lower than the second region cost based on a direction of travel of the vehicle and the direction of travel associated with the first drivable region 106 and/or the second drivable region 108. As can be understood, when the direction of travel of the vehicle 104 is the same as the direction of travel of the first drivable region 106 and is different from the direction of travel of the second drivable region 108, the first region cost can be lower than the second region cost. & para. [0037]: Based at least in part on the updated drivable region 214, the vehicle 104 can determine a candidate trajectory 216 associated with the partial lane expansion action. The candidate trajectory 216 can cause the vehicle 104 to partially traverse into the second driving lane 112 which can allow the vehicle 104 to safely pass the object 116.).
However Gier does not explicitly disclose
one or more of determining or receiving, based at least in part on a capable acceleration of the blocking object and a current velocity of the vehicle, a first region proximate the blocking object,
the first region including a first weight to increase a first cost associated with controlling the vehicle within the first region;
the second region including a second weight to decrease a second cost associated with controlling the vehicle within the second region;
determining, based at least in part on the first weight and the first cost, a first modified cost;
determining, based at least in part on the second weight and the second cost, a second modified cost;
determining, based at least in part on the first modified cost and the second modified cost, a cost associated with a trajectory for controlling the vehicle proximate the blocking object.
Tsuchiya teaches
one or more of determining or receiving, based at least in part on an acceleration of the blocking object and a current velocity of the vehicle, a first region proximate the blocking object (see at least Tsuchiya, Fig. 4 & para. [0070-0072]: The risk area deriver 138 may adjust the risk area according to the speed or the moving direction of the physical object. For example, the risk area deriver 138 may derive a risk area based on the risk potential by increasing the risk potential as an absolute velocity or absolute acceleration of the physical object increases. Instead of (or in addition to) the absolute velocity or the absolute acceleration of the physical object, the risk potential may be appropriately decided in accordance with a relative velocity and relative acceleration between the host vehicle M and the physical object, time to collision (TTC), a predicted contact position, or the like. The risk potential may be adjusted in accordance with a surrounding situation such as a road shape, a degree of congestion, weather, or time of day.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gier to incorporate the teaching of one or more of determining or receiving, based at least in part on a predicted acceleration of the blocking object and a current velocity of the vehicle, a first region proximate the blocking object of Tsuchiya, with a reasonable expectation of success, in order for the accuracy of the position, size, and type of the physical object to be improved and more accurate physical object recognition (see at least Tsuchiya, para. [0091]).
Pazhayampallil teaches
one or more of determining or receiving, based at least in part on a capable acceleration of the blocking object, a first region proximate the blocking object (see at least Pazhayampallil, para. [0056]: The autonomous vehicle can then implement methods and techniques described above: to recalculate a critical time of the autonomous vehicle based on the autonomous vehicle's speed during this second scan cycle; and to recalculate a future state boundary of the object from the current time to this revised critical time based on the true (absolute or relative) velocity of the object (rather than the worst-case speed of a generic object), the angular velocity of the object, and the maximum possible acceleration of a generic object—limited by the maximum possible speed of a generic object—from the current time to the revised critical time. & para. [0064]: The autonomous vehicle can repeat this process for the subsequent scan cycle, including: further revising the maximum possible azimuthal speed of the virtual object—along the azimuthal direction relative to the autonomous vehicle—based on the length of the first object and the time interval over the set of scan images in which the autonomous vehicle detected the first object; recalculating maximum possible speeds and accelerations of the virtual object in various directions based on this maximum possible azimuthal speed of the virtual object; and refining the virtual future state boundary of the virtual object based on these maximum possible speeds, maximum possible accelerations, and the maximum possible azimuthal speed of the virtual object.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gier to incorporate the teaching of one or more of determining or receiving, based at least in part on a capable acceleration of the blocking object, a first region proximate the blocking object of Pazhayampallil, with a reasonable expectation of success, in order to improve the autonomous vehicle certainty of the motion of the object in the future (see at least Pazhayampallil, para. [0177]).
Phillips 351` teaches
the first region including a first weight to increase a first cost associated with controlling the vehicle within the first region (see at least Phillips 351`, para. [0062]: In some implementations, different scoring factors can be associated with the different first and second spatial regions such that trajectory paths determined relative to such spatial regions can be costed accordingly in a customizable fashion relative to desired action of the autonomous vehicle relative to the spatial regions of the gridlock constraint. Para. [0120]: Thus, if a candidate motion plan approaches a proximate object of interest, the first cost increases, thereby discouraging (e.g., through increased cost penalization) the autonomous vehicle from selecting motion plans that come undesirably close to the object.);
the second region including a second weight associated with controlling the vehicle within the second region (see at least Phillips 351`, para. [0062]: In some implementations, different scoring factors can be associated with the different first and second spatial regions such that trajectory paths determined relative to such spatial regions can be costed accordingly in a customizable fashion relative to desired action of the autonomous vehicle relative to the spatial regions of the gridlock constraint.);
determining, based at least in part on the first weight and the first cost, a first modified cost (see at least Phillips 351`, para. [0065]: With more particular reference to determining a low-cost trajectory path, the score generated for each candidate trajectory path can include one or more scoring factors, including but not limited to costs, discounts and/or rewards associated with aspects of a candidate trajectory path for use in evaluation of a cost function or other scoring equation. para. [0117]: According to an aspect of the present disclosure, the total cost can be based at least in part on one or more cost functions 304. In one example implementation, the total cost equals the sum of all costs minus the sum of all rewards and the optimization planner attempts to minimize the total cost. The cost functions 304 can be evaluated by a penalty/reward generator 302.);
determining, based at least in part on the second weight and the second cost, a second modified cost (see at least Phillips 351`, para. [0065]: With more particular reference to determining a low-cost trajectory path, the score generated for each candidate trajectory path can include one or more scoring factors, including but not limited to costs, discounts and/or rewards associated with aspects of a candidate trajectory path for use in evaluation of a cost function or other scoring equation. para. [0117]: According to an aspect of the present disclosure, the total cost can be based at least in part on one or more cost functions 304. In one example implementation, the total cost equals the sum of all costs minus the sum of all rewards and the optimization planner attempts to minimize the total cost. The cost functions 304 can be evaluated by a penalty/reward generator 302.);
determining, based at least in part on the first modified cost and the second modified cost, a cost associated with a trajectory for controlling the vehicle (see at least Phillips 351`, para. [0065]: With more particular reference to determining a low-cost trajectory path, the score generated for each candidate trajectory path can include one or more scoring factors, including but not limited to costs, discounts and/or rewards associated with aspects of a candidate trajectory path for use in evaluation of a cost function or other scoring equation. para. [0117]: According to an aspect of the present disclosure, the total cost can be based at least in part on one or more cost functions 304. In one example implementation, the total cost equals the sum of all costs minus the sum of all rewards and the optimization planner attempts to minimize the total cost. The cost functions 304 can be evaluated by a penalty/reward generator 302.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gier to incorporate the teaching of the first region including a first weight to increase a first cost associated with controlling the vehicle within the first region, determining, based at least in part on the second weight and the second cost, a second modified cost; determining, based at least in part on the first modified cost and the second modified cost, a cost associated with a trajectory for controlling the vehicle of Phillips 351`, with a reasonable expectation of success, in order to identify an appropriate motion path through such surrounding environment (see at least Phillips 351`, para. [0003]).
Lee teaches
one or more of determining or receiving a first region proximate the blocking object (see at least Lee, Figs. 2-8),
the first region including a weight to increase a first cost associated with controlling the vehicle within the first region (see at least Lee, para. [0050]: In addition, the cost allocator 120 may allocate a relatively high cost as an area resulting from division of the space is closer to the obstacle, and a relatively low cost as an area resulting from division of the space is further away from the obstacle, and then generate a cost map by storing a cost value thereof along with map information and obstacle information.);
one or more of determining or receiving a second region adjacent the first region (see at least Lee, Figs. 2-8),
the second region including a weight to decrease a second cost associated with controlling the vehicle within the second region (see at least Lee, para. [0050]: In addition, the cost allocator 120 may allocate a relatively high cost as an area resulting from division of the space is closer to the obstacle, and a relatively low cost as an area resulting from division of the space is further away from the obstacle, and then generate a cost map by storing a cost value thereof along with map information and obstacle information.);
determining, based at least in part on the weight and the first cost, a first modified cost (see at least Lee, para. [0054]: In addition, the cost map generation module 100 may differently allocate a cost to areas in such a way that the cost decreases linearly as the distance increases from the area occupied by the obstacle as shown in FIG. 2. Of course, it is also possible to allocate a cost such that the cost decreases rapidly non-linearly as the distance increases. & para. [0084]: In addition, as shown in FIG. 10, when the driving condition requested from the mobile robot is to preferentially drive in an area in which a safe distance from an obstacle is secured, the cost function factor determiner 310 may determine, as a cost function factor, safety of the cost map or congestion of the cost map, and the path selector 320 may select a path with the minimum cost as the optimal path by calculating a cumulative or average cost value in a case where the mobile robot drives along each of paths by referring to a cost value matched to each area on the cost map.);
determining, based at least in part on the weight and the second cost, a second modified cost (see at least Lee, para. [0054]: In addition, the cost map generation module 100 may differently allocate a cost to areas in such a way that the cost decreases linearly as the distance increases from the area occupied by the obstacle as shown in FIG. 2. Of course, it is also possible to allocate a cost such that the cost decreases rapidly non-linearly as the distance increases. & para. [0084]: In addition, as shown in FIG. 10, when the driving condition requested from the mobile robot is to preferentially drive in an area in which a safe distance from an obstacle is secured, the cost function factor determiner 310 may determine, as a cost function factor, safety of the cost map or congestion of the cost map, and the path selector 320 may select a path with the minimum cost as the optimal path by calculating a cumulative or average cost value in a case where the mobile robot drives along each of paths by referring to a cost value matched to each area on the cost map.);
determining, based at least in part on the first modified cost and the second modified cost, a cost associated with a trajectory for controlling the vehicle proximate the blocking object (see at least Lee, para. [0050]: In addition, the cost allocator 120 may allocate a relatively high cost as an area resulting from division of the space is closer to the obstacle, and a relatively low cost as an area resulting from division of the space is further away from the obstacle, and then generate a cost map by storing a cost value thereof along with map information and obstacle information. para. [0054]: In addition, the cost map generation module 100 may differently allocate a cost to areas in such a way that the cost decreases linearly as the distance increases from the area occupied by the obstacle as shown in FIG. 2. Of course, it is also possible to allocate a cost such that the cost decreases rapidly non-linearly as the distance increases. & para. [0084]: In addition, as shown in FIG. 10, when the driving condition requested from the mobile robot is to preferentially drive in an area in which a safe distance from an obstacle is secured, the cost function factor determiner 310 may determine, as a cost function factor, safety of the cost map or congestion of the cost map, and the path selector 320 may select a path with the minimum cost as the optimal path by calculating a cumulative or average cost value in a case where the mobile robot drives along each of paths by referring to a cost value matched to each area on the cost map.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gier to incorporate the teaching of the first region including a weight to increase a first cost associated with controlling the vehicle within the first region, the second region including a weight to decrease a second cost associated with controlling the vehicle within the second region, determining, based at least in part on the weight and the first cost, a first modified cost, determining, based at least in part on the weight and the second cost, a second modified cost, determining, based at least in part on the first modified cost and the second modified cost, a cost associated with a trajectory for controlling the vehicle proximate the blocking object, of Lee, with a reasonable expectation of success, in order for improving the convenience of cost allocation for each area (see at least Lee, para. [0048]).
As per claim 15 Gier discloses
wherein the first cost is associated with progress to a destination along the trajectory (see at least Gier, para. [0023]: In general, the one or more costs can include, but is not limited to a reference cost, an obstacle cost, a lateral cost, a longitudinal cost, a region cost, a width cost, an indicator cost, an action switch cost, an action cost, a utilization cost, and the like.).
As per claim 16 Gier discloses
wherein a first size of the first region is determined based at least in part on at least one of:
a type of the blocking object,
a size of the blocking object,
a kinematic of the vehicle,
semantic information of an environment proximate the vehicle (see at least Gier, para. [0015]: The vehicle can be configured to use the map data to determine the first drivable region 106 and the second drivable region 108. For example, the map data can indicate that the first drivable region is associated with a first driving lane 110 and that the second drivable region is associated with a second driving lane 112. Additionally, the map data can indicate that the first drivable region 106 is associated with a first direction of travel and that the second drivable region 108 is associated with a second direction of travel that is different from the first direction of travel. In some instances, the map data can include road marker data (e.g., single yellow markers, double yellow markers, single white markers, double white markers, solid markers, broken markers, and the like).),
a velocity of the vehicle,
a difference of speed between the vehicle and the blocking object, or an orientation of the blocking object.
As per claim 19 Gier discloses
wherein controlling the vehicle in accordance with the trajectory causes the vehicle to move at least partially into an oncoming lane of traffic (see at least Gier, para. [0037]: Based at least in part on the updated drivable region 214, the vehicle 104 can determine a candidate trajectory 216 associated with the partial lane expansion action. The candidate trajectory 216 can cause the vehicle 104 to partially traverse into the second driving lane 112 which can allow the vehicle 104 to safely pass the object 116.).
As per claim 21 Gier does not explicitly disclose
wherein the second region surrounds the first region.
Lee teaches
wherein the second region surrounds the first region (see at least Lee, Fig. 2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gier to incorporate the teaching wherein the second region surrounds the first region, of Lee, with a reasonable expectation of success, in order for improving the convenience of cost allocation for each area (see at least Lee, para. [0048]).
Claim(s) 4, 9, & 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gier, in view of Tsuchiya, in view of Pazhayampallil, in view of Phillips 351`, in view of Lee, in view of US 2021/0394749A1 (“Horigome”).
As per claim 4 Gier does not explicitly disclose
wherein a size of the second region is determined based at least in part on at least one of:
one of:
a first velocity of the autonomous vehicle,
a type of the object,
a predicted acceleration of the object,
a second velocity of the object, or a second size of the object.
Horigome teaches
wherein a size of the second region is determined based at least in part on at least one of:
one of:
a first velocity of the autonomous vehicle,
a type of the object,
a predicted acceleration of the object,
a second velocity of the object (see at least Horigome, para. [0054]: The second safe area SA2 is set based on a predetermined rule, e.g., an area of several meters around the target object is considered as a range where the collision is unavoidable. The second safe area setting unit 122 is configured to be able to set the second safe area SA2 in consideration of the speed of traveling vehicles and the speed of pedestrians.), or
a second size of the object (see at least Horigome, para. [0079]: The target object recognition unit 121 recognizes the target object based on the existing predetermined rule, and thus, can accurately recognize the size of the target object. In addition, the second safe area setting unit 122 sets the second safe area SA2 based on a predetermined rule, e.g., an area of several meters around the target object is considered as a range where the collision is unavoidable.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gier to incorporate the teaching of wherein a size of the second region is determined based at least in part on at least one of: one of: a first velocity of the autonomous vehicle, a type of the object, a predicted acceleration of the object, a second velocity of the object, or a second size of the object of Horigome, with a reasonable expectation of success, in order to improve the functional safety level of an arithmetic device having the function of using deep learning (see at least Horigome, para. [0022]).
As per claim 9 Gier does not explicitly disclose
wherein a size of the second region is determined based at least in part on at least one of:
a first velocity of the vehicle,
a type of the blocking object,
a predicted acceleration of the blocking object,
a second velocity of the blocking object, or a second size of the blocking object.
Horigome teaches
wherein a size of the second region is determined based at least in part on at least one of:
a first velocity of the vehicle,
a type of the blocking object,
a predicted acceleration of the blocking object,
a second velocity of the blocking object (see at least Horigome, para. [0054]: The second safe area SA2 is set based on a predetermined rule, e.g., an area of several meters around the target object is considered as a range where the collision is unavoidable. The second safe area setting unit 122 is configured to be able to set the second safe area SA2 in consideration of the speed of traveling vehicles and the speed of pedestrians.), or
a second size of the blocking object (see at least Horigome, para. [0079]: The target object recognition unit 121 recognizes the target object based on the existing predetermined rule, and thus, can accurately recognize the size of the target object. In addition, the second safe area setting unit 122 sets the second safe area SA2 based on a predetermined rule, e.g., an area of several meters around the target object is considered as a range where the collision is unavoidable.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gier to incorporate the teaching of wherein a size of the second region is determined based at least in part on at least one of: one of: a first velocity of the autonomous vehicle, a type of the blocking object, a predicted acceleration of the blocking object, a second velocity of the blocking object, or a second size of the blocking object of Horigome, with a reasonable expectation of success, in order to improve the functional safety level of an arithmetic device having the function of using deep learning (see at least Horigome, para. [0022]).
As per claim 17 Gier does not explicitly disclose
wherein a size of the second region is determined based at least in part on at least one of:
first velocity of the vehicle,
a type of the blocking object,
a second velocity of the blocking object, or a second size of the blocking object.
Horigome teaches
wherein a size of the second region is determined based at least in part on at least one of:
first velocity of the vehicle,
a type of the blocking object,
a second velocity of the blocking object (see at least Horigome, para. [0054]: The second safe area SA2 is set based on a predetermined rule, e.g., an area of several meters around the target object is considered as a range where the collision is unavoidable. The second safe area setting unit 122 is configured to be able to set the second safe area SA2 in consideration of the speed of traveling vehicles and the speed of pedestrians.), or
a second size of the blocking object (see at least Horigome, para. [0079]: The target object recognition unit 121 recognizes the target object based on the existing predetermined rule, and thus, can accurately recognize the size of the target object. In addition, the second safe area setting unit 122 sets the second safe area SA2 based on a predetermined rule, e.g., an area of several meters around the target object is considered as a range where the collision is unavoidable.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gier to incorporate the teaching of wherein a size of the second region is determined based at least in part on at least one of: one of: a first velocity of the autonomous vehicle, a type of the blocking object, a predicted acceleration of the blocking object, a second velocity of the blocking object, or a second size of the blocking object of Horigome, with a reasonable expectation of success, in order to improve the functional safety level of an arithmetic device having the function of using deep learning (see at least Horigome, para. [0022]).
Claim(s) 5, 10, 12, 18, & 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gier, in view of Tsuchiya, in view of Pazhayampallil, in view of Phillips 351`, in view of Lee, in view of US 2021/0114617A1 (“Phillips 617` ”).
As per claim 5 Gier does not explicitly disclose
wherein the second cost is associated with at least a comfort cost based at least in part on one or more of an acceleration or a jerk associated with the trajectory, or a policy cost associated with the autonomous vehicle complying with one or more laws or policies when controlled in accordance with the trajectory.
Phillips 617` teaches
wherein the second cost is associated with at least a comfort cost based at least in part on one or more of an acceleration or a jerk associated with the trajectory, or a policy cost associated with the autonomous vehicle complying with one or more laws or policies when controlled in accordance with the trajectory (see at least Phillips 617`, para. [0070]: For example, the vehicle computing system ( e.g., a policy scoring system) can generate a cost ( or component of a cost) for a particular trajectory based on the degree to which it complies with one or more vehicle motion policies of the motion planning system. A lower cost can represent a more preferable trajectory. For example, the vehicle computing system may include one or more vehicle motion policies that represent legal rules that govern the area in which the autonomous vehicle is traveling. Each rule may be assigned a weight and cost can be assigned to a trajectory based on the degree to which it follows that rule. para. [0122]: The scoring system 220 can determine a cost for each respective trajectory in the plurality of trajectories based, at least in part, on the data obtained from the one or more sensor(s). For instance, the scoring system 220 can score each trajectory against a cost function that ensures safe, efficient, and comfortable vehicle motion. A cost function can be encoded for one or more of: the avoidance of object collision, keeping the autonomous vehicle on the travel way/within lane boundaries, preferring gentle accelerations to harsh ones, etc. As further described herein, the cost function(s) can consider vehicle dynamics parameters ( e.g., to keep the ride smooth, acceleration, jerk, etc.) and/or map parameters (e.g., speed limits, stops, travel way boundaries, etc.).).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gier to incorporate the teaching of wherein the second cost is associated with at least a comfort cost based at least in part on one or more of an acceleration or a jerk associated with the trajectory, or a policy cost associated with the autonomous vehicle complying with one or more laws or policies when controlled in accordance with the trajectory of Phillips 617`, with a reasonable expectation of success, in order to allow autonomous vehicles to be controlled in a manner that is both safer and more efficient (see at least Phillips 617`, para. [0078]).
As per claim 10 Gier does not explicitly disclose
wherein determining the second cost is associated with one or more of comfort or policy along the trajectory.
Phillips 617` teaches
wherein determining the second cost is associated with one or more of comfort or policy along the trajectory (see at least Phillips 617`, para. [0070]: For example, the vehicle computing system ( e.g., a policy scoring system) can generate a cost ( or component of a cost) for a particular trajectory based on the degree to which it complies with one or more vehicle motion policies of the motion planning system. A lower cost can represent a more preferable trajectory. For example, the vehicle computing system may include one or more vehicle motion policies that represent legal rules that govern the area in which the autonomous vehicle is traveling. Each rule may be assigned a weight and cost can be assigned to a trajectory based on the degree to which it follows that rule. para. [0122]: The scoring system 220 can determine a cost for each respective trajectory in the plurality of trajectories based, at least in part, on the data obtained from the one or more sensor(s). For instance, the scoring system 220 can score each trajectory against a cost function that ensures safe, efficient, and comfortable vehicle motion. A cost function can be encoded for one or more of: the avoidance of object collision, keeping the autonomous vehicle on the travel way/within lane boundaries, preferring gentle accelerations to harsh ones, etc. As further described herein, the cost function(s) can consider vehicle dynamics parameters ( e.g., to keep the ride smooth, acceleration, jerk, etc.) and/or map parameters (e.g., speed limits, stops, travel way boundaries, etc.).).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gier to incorporate the teaching of wherein determining the second cost is associated with one or more of comfort or policy along the trajectory of Phillips 617`, with a reasonable expectation of success, in order to allow autonomous vehicles to be controlled in a manner that is both safer and more efficient (see at least Phillips, para. [0078]).
As per claim 12 Gier does not explicitly disclose
wherein the second cost is associated with at least one of:
a comfort cost based at least in part on one or more of an acceleration or a jerk associated with the trajectory, or a policy cost associated with the vehicle complying with one or more laws or policies when controlled in accordance with the trajectory.
Phillips 617` teaches
wherein the second cost is associated with at least one of:
a comfort cost based at least in part on one or more of an acceleration or a jerk associated with the trajectory, or a policy cost associated with the vehicle complying with one or more laws or policies when controlled in accordance with the trajectory (see at least Phillips 617`, para. [0070]: For example, the vehicle computing system ( e.g., a policy scoring system) can generate a cost ( or component of a cost) for a particular trajectory based on the degree to which it complies with one or more vehicle motion policies of the motion planning system. A lower cost can represent a more preferable trajectory. For example, the vehicle computing system may include one or more vehicle motion policies that represent legal rules that govern the area in which the autonomous vehicle is traveling. Each rule may be assigned a weight and cost can be assigned to a trajectory based on the degree to which it follows that rule. para. [0122]: The scoring system 220 can determine a cost for each respective trajectory in the plurality of trajectories based, at least in part, on the data obtained from the one or more sensor(s). For instance, the scoring system 220 can score each trajectory against a cost function that ensures safe, efficient, and comfortable vehicle motion. A cost function can be encoded for one or more of: the avoidance of object collision, keeping the autonomous vehicle on the travel way/within lane boundaries, preferring gentle accelerations to harsh ones, etc. As further described herein, the cost function(s) can consider vehicle dynamics parameters ( e.g., to keep the ride smooth, acceleration, jerk, etc.) and/or map parameters (e.g., speed limits, stops, travel way boundaries, etc.).).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gier to incorporate the teaching of wherein the second cost is associated with at least one of: a comfort cost based at least in part on one or more of an acceleration or a jerk associated with the trajectory, or a policy cost associated with the vehicle complying with one or more laws or policies when controlled in accordance with the trajectory of Phillips 617`, with a reasonable expectation of success, in order to allow autonomous vehicles to be controlled in a manner that is both safer and more efficient (see at least Phillips 617`, para. [0078]).
As per claim 18 Gier does not explicitly disclose
wherein determining the second cost is associated with one or more of comfort or policy along the trajectory.
Phillips 617` teaches
wherein determining the second cost is associated with one or more of comfort or policy along the trajectory (see at least Phillips 617`, para. [0070]: For example, the vehicle computing system ( e.g., a policy scoring system) can generate a cost ( or component of a cost) for a particular trajectory based on the degree to which it complies with one or more vehicle motion policies of the motion planning system. A lower cost can represent a more preferable trajectory. For example, the vehicle computing system may include one or more vehicle motion policies that represent legal rules that govern the area in which the autonomous vehicle is traveling. Each rule may be assigned a weight and cost can be assigned to a trajectory based on the degree to which it follows that rule. para. [0122]: The scoring system 220 can determine a cost for each respective trajectory in the plurality of trajectories based, at least in part, on the data obtained from the one or more sensor(s). For instance, the scoring system 220 can score each trajectory against a cost function that ensures safe, efficient, and comfortable vehicle motion. A cost function can be encoded for one or more of: the avoidance of object collision, keeping the autonomous vehicle on the travel way/within lane boundaries, preferring gentle accelerations to harsh ones, etc. As further described herein, the cost function(s) can consider vehicle dynamics parameters ( e.g., to keep the ride smooth, acceleration, jerk, etc.) and/or map parameters (e.g., speed limits, stops, travel way boundaries, etc.).)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gier to incorporate the teaching of wherein determining the second cost is associated with one or more of comfort or policy along the trajectory of Phillips 617`, with a reasonable expectation of success, in order to allow autonomous vehicles to be controlled in a manner that is both safer and more efficient (see at least Phillips, para. [0078]).
As per claim 20 Gier does not explicitly disclose
wherein the second cost is associated with at least one of:
a comfort cost based at least in part on one or more of an acceleration or a jerk associated with the trajectory, or a policy cost associated with the vehicle complying with one or more laws or policies when controlled in accordance with the trajectory.
Phillips 617` teaches
wherein the second cost is associated with at least one of:
a comfort cost based at least in part on one or more of an acceleration or a jerk associated with the trajectory, or a policy cost associated with the vehicle complying with one or more laws or policies when controlled in accordance with the trajectory (see at least Phillips 617`, para. [0070]: For example, the vehicle computing system ( e.g., a policy scoring system) can generate a cost ( or component of a cost) for a particular trajectory based on the degree to which it complies with one or more vehicle motion policies of the motion planning system. A lower cost can represent a more preferable trajectory. For example, the vehicle computing system may include one or more vehicle motion policies that represent legal rules that govern the area in which the autonomous vehicle is traveling. Each rule may be assigned a weight and cost can be assigned to a trajectory based on the degree to which it follows that rule. para. [0122]: The scoring system 220 can determine a cost for each respective trajectory in the plurality of trajectories based, at least in part, on the data obtained from the one or more sensor(s). For instance, the scoring system 220 can score each trajectory against a cost function that ensures safe, efficient, and comfortable vehicle motion. A cost function can be encoded for one or more of: the avoidance of object collision, keeping the autonomous vehicle on the travel way/within lane boundaries, preferring gentle accelerations to harsh ones, etc. As further described herein, the cost function(s) can consider vehicle dynamics parameters ( e.g., to keep the ride smooth, acceleration, jerk, etc.) and/or map parameters (e.g., speed limits, stops, travel way boundaries, etc.).).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gier to incorporate the teaching of wherein the second cost is associated with at least one of: a comfort cost based at least in part on one or more of an acceleration or a jerk associated with the trajectory, or a policy cost associated with the vehicle complying with one or more laws or policies when controlled in accordance with the trajectory of Phillips 617`, with a reasonable expectation of success, in order to allow autonomous vehicles to be controlled in a manner that is both safer and more efficient (see at least Phillips 617`, para. [0078]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/MOHAMED ABDO ALGEHAIM/Primary Examiner, Art Unit 3668