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 .
This is a Final Rejection on the merits. Claims 1, 3, 5-9, 11, and 29-40 are currently pending and are addressed below.
Response to Amendments
The amendment filed on January 29th, 2026 has been considered and entered. Accordingly, claims 1, 29, 31-33, and 36 have been amended. Claims 2-4, 10, and 12-28 have been canceled. Claims 39-40 have been newly added.
Response to Arguments
The Applicant states (Amend. 9) that the rejections of claims 33-35 were not provided in the office action. The examiner states that claims 33-35 correspond to claims 7-9 and the appropriate correction has been made below.
The previous rejection of claims 1, 3, 5-9, 11, and 29-40 under 35 USC 112(b) has been overcome due to the applicant’s amendments.
The applicant’s arguments with respect to claims 1, 3, 5-9, 11, and 29-40 have been considered but are moot in view of the newly formulated grounds of rejections necessitated by the applicant’s amendments, however, at least one pertinent argument remains.
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3, 6, 29-30, 32, and 38-39 are rejected under 35 U.S.C. 103 as being unpatentable over Yang (US 20180273032 A1) (“Yang”) in view of Seegmiller (US 20210107566 A1) (“Seegmiller”) in view of Subramanian (US 20220176995 A1) (“Subramanian”) in view of Kelly (US 20190346275 B1) (“Kelly”).
With respect to claim 1, Yang teaches a method for route planning, comprising:
obtaining or planning a lane-level route to the destination using a map (A lane-level route is determined based on start point, destination, and map information: See at least Yang Paragraph 9),
and controlling the AV to traverse the lane-level route (Vehicle is controlled autonomously to travel the determined lane-level route: See at least Yang Paragraph 7).
Yang, however fails to explicitly disclose wherein the lane-level route is obtained based on a policy that uses two or more objectives specified by a user; obtaining or planning, with a lane-level route planner, according to the policy, locations along the lane-level route where an autonomous vehicle (AV) is to be controlled to make lane changes, prior to a planned lane change from a first lane segment to a second lane segment along the lane-level route, obtaining or planning, according to the a lane-level contingency route from the first lane segment to the destination in a case of a failure to control the AV to make the planned lane change from the first lane segment to the second lane segment; in response to the failure to control the AV to make the planned lane change from the first lane segment to the second lane segment, controlling the AV to traverse the lane-level contingency route from the first lane segment to the destination and associating each of the two or more objectives with a semantic label that indicates route preferences of the user; and providing the semantic label to the lane-level route planner to plan each of the lane-level contingency route, wherein a slack variable1, related toa preference ordering of the two or more objectives, is provided with each of the lane-level contingency routes so that the lane-level contingency route is associated with the two or more objectives of the slack variable is selected.
Seegmiller, however, teaches obtaining or planning, with a lane-level route planner, according to the policy, locations along the lane-level route where an autonomous vehicle (AV) is to be controlled to make lane changes, prior to a planned lane change from a first lane segment to a second lane segment along the lane-level route, obtaining or planning, according to the a lane-level contingency route from the first lane segment to the destination in a case of a failure to control the AV to make the planned lane change from the first lane segment to the second lane segment; in response to the failure to control the AV to make the planned lane change from the first lane segment to the second lane segment, controlling the AV to traverse the lane-level contingency route from the first lane segment to the destination (See at least Seegmiller FIG. 5B and Paragraph 50 “For example, as shown in FIG. 5B, the nominal route 500 of FIG. 5A may be expanded to include neighboring lane segments 521, 522, 514, 515, 516, and 517, as well as contingency lane segments 518 and 519 (in case the autonomous vehicle fails to make the lane change from lane 510 to 520), and contingency lane segments 528 and 529 (in case the autonomous vehicle fails to execute a turn by taking lane segment 530 through the intersection 540). Such contingency extensions to corridors 510 and 520 enable the autonomous vehicle to remain on the route in situations when it fails to make a required lane change or turn, until it is successfully re-routed. Lane changes are permissible from lane segments 511 to 521, lane segments 513 and/or 514 to 523 and/or 524, and lane segments 516 and/or 517 to 526 and/or 527. The expanded route representation may also include lane segments in intersections (512 to 522, 515 to 525, and 518 to 528), where lane change is not permitted.” | Paragraphs 68-69 “The system may then compute (333) dynamic costs associated with each of the candidate trajectories and identify (334) the candidate trajectory that has the minimum dynamic cost as the vehicle trajectory to be used for traversing the local region. The dynamic cost may be computed based on for example, the real-time perception information of the autonomous vehicle, location and predicted trajectories of objects in the autonomous vehicle's environment, locations of intersections in the corridors, passenger safety and comfort, location of permissible lane change regions in the corridors, the lane change location of that reference trajectory, static costs associated with various lane segments, the optimal route, contingency lane segments, or the like. For example, if a reference trajectory will take the autonomous vehicle into the path of another vehicle (based on the other vehicle's predicted trajectory), such a trajectory may be discarded as being too costly to execute. Similarly, a trajectory that requires the autonomous vehicle to make jerky movements during lane change may be discarded by assigning it a higher cost with respect to passenger comfort. In another example, the presence of another vehicle in a permissible lane change region in FIG. 5C may increase the cost of a lane change trajectory and the system may select the stay in lane trajectory as the best trajectory for traversing the local region of FIG. 5C. The stay-in-lane trajectory 558 for local region 550 shown in FIG. 5C, and the stay-in-lane trajectory 568 for local region 560 is shown in FIG. 5D. Similarly, two of the possible lane change trajectories 559(a) and 559(b) are shown in FIG. 5D.”).
It would have been obvious to one of ordinary skill in the art to have modified the method of Yang to include obtaining or planning, with a lane-level route planner, according to the policy, locations along the lane-level route where an autonomous vehicle (AV) is to be controlled to make lane changes, prior to a planned lane change from a first lane segment to a second lane segment along the lane-level route, obtaining or planning, according to the a lane-level contingency route from the first lane segment to the destination in a case of a failure to control the AV to make the planned lane change from the first lane segment to the second lane segment; in response to the failure to control the AV to make the planned lane change from the first lane segment to the second lane segment, controlling the AV to traverse the lane-level contingency route from the first lane segment to the destination, as taught by Seegmiller as disclosed above, in order to ensure continuous autonomous travel (Seegmiller Abstract “A method and a system for maneuvering an autonomous vehicle is disclosed. ”).
Yang in view of Seegmiller fail to explicitly disclose wherein the lane-level route is obtained based on a policy that uses two or more objectives specified by a user and associating each of the two or more objectives with a semantic label that indicates route preferences of the user; and providing the semantic label to the lane-level route planner to plan each of the lane-level contingency route, wherein a slack variable2, related to a preference ordering of the two or more objectives, is provided with each of the lane-level contingency routes so that the lane-level contingency route is associated with the two or more objectives of the slack variable is selected.
Subramanian teaches wherein the lane-level route is obtained based on a policy that uses two or more objectives specified by a user (See at least Subramanian Paragraph 37 “The vehicle computing system can generate one or more respective cost(s) for each respective trajectory in the plurality of trajectories based, at least in part, on the obtained sensor data. For instance, the vehicle computing system can score each trajectory against one or more cost function(s) designed to score a trajectory based on one or more motion planning criteria associated with the safe, efficient, and comfortable motion of a vehicle. The cost function(s), for example, can be encoded for the avoidance of object interferences, for the autonomous vehicle to stay on the travel way/within lane boundaries, prefer 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.). The cost function(s) can also, or alternatively, take into account at least one of the following object cost(s): interference costs (e.g., cost of avoiding/experiencing potential interference with another object, minimization of speed, etc.)” | Paragraph 58 “In some implementations, the verification system can obtain user input indicative of a costing change to at least one of the plurality of sub-cost functions and/or the one or more relational propositions. In such a case, the verification system can generate the verification data based on the costing change. For example, the costing change can include an additional sub-cost function in addition to the plurality of sub-cost functions. The verification system can generate the one or more cost modifications for the plurality of sub-cost functions based on the additional sub-cost function. As another example, the costing change can include an additional relational proposition in addition to the one or more relational propositions. The verification system can generate the one or more cost modifications for the plurality of sub-cost functions based on the additional relational proposition.”).
Subramanian teaches that the lane-level route is obtained using two or more objectives specified by a user to improve safety, efficiency, and a “comfortable motion of a vehicle” such that it would have been obvious to one of ordinary skill in the art before the effective filing date to have modified the method of Yang in view of Seegmiller to implement a preference for lane-level route using two or more objectives since the “cost functions” of Subramanian at least suggests the preference of “gentle accelerations to harsh ones”, “consider … speed limits”, and the “minimization of speeds”, which in turn improves comfort (Subramanian Paragraph 37 “the vehicle computing system can score each trajectory against one or more cost function(s) designed to score a trajectory based on one or more motion planning criteria associated with the safe, efficient, and comfortable motion of a vehicle. The cost function(s), for example, can be encoded for the avoidance of object interferences, for the autonomous vehicle to stay on the travel way/within lane boundaries, prefer 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.). The cost function(s) can also, or alternatively, take into account at least one of the following object cost(s): interference costs (e.g., cost of avoiding/experiencing potential interference with another object, minimization of speed, etc.)”.).
Yang in view of Seegmiller in view of Subramanian fail to explicitly disclose associating each of the two or more objectives with a semantic label that indicates route preferences of the user; and providing the semantic label to the lane-level route planner to plan each of the lane-level contingency route, wherein a slack variable3, related to a preference ordering of the two or more objectives, is provided with each of the lane-level contingency routes so that the lane-level contingency route is associated with the two or more objectives of the slack variable is selected.
Kelly teaches associating each of the two or more objectives with a semantic label that indicates route preferences of the user; and providing the semantic label to the lane-level route planner to plan each of the lane-level contingency route, wherein a slack variable4, related to a preference ordering of the two or more objectives, is provided with each of the lane-level contingency routes so that the lane-level contingency route is associated with the two or more objectives of the slack variable is selected See at least Kelly FIGS. 3 and 11 and Paragraphs 306-307 “FIG. 3 shows a representation of example process steps that may be undertaken to determine the journey definition (part A in FIG. 2). The process steps represented in FIG. 3 comprise determining a nominal journey understanding (part A.1), obtaining journey objectives and destination objectives (part A.2) and development of a directed graph that is representative of routing options. Each of these process steps is described in detail below. The destination objectives are a particular type of end objective. Destination objectives relate to objectives to be achieved on arrival at the destination location of the ‘global’ journey. A different, but analogous, type of end objectives may relate to objectives to be achieved at the end of a ‘local’ journey.” | Paragraphs 347-348 “If confidence is low that one or many of the objectives can be met (for example, none of the routes in the directed graph of routing options are expected to meet one or more of the objectives, or the directed graph of routing options comprises an insufficient number of potential routes that should meet some or all of the objectives with sufficient certainty to satisfy a confidence threshold), the process may proceed to part A.4. In part A.4, the user may be informed that confidence in meeting the objectives is low (for example, by displaying a warning message on the screen), at which time the process may return to part A.2 where the user may adjust one or more of the objectives to improve confidence. Alternatively, the user may indicate that they wish to proceed anyway, in which case the process proceeds to completion of the journey definition.” | Paragraphs 458-459 “Optionally, in Step S2030, it may be considered whether or not the extrapolated value (or value of current time metric and/or efficiency metric in the case where the recommended action is selected on arrival at the upcoming node) exceeds time metric and/or efficiency metric in all of the probabilistic states identifying the upcoming node by more than a threshold amount. Exceeding the values of the time metric and/or efficiency in all of the probabilistic states identifying the upcoming node by more than a threshold amount may indicate that time and/or efficiency for the journey so far are progressing poorly, thereby jeopardising the achievement of the destination objectives. The threshold amount may be set to any value, for example 0 minutes, 1 minute, 2 minutes, 4.3 minutes, 0 litres/gallons, 0.1 litres, 3 mpg, 0 kWh, 0.5 kWh, 0.8 kWh, etc). For example, if it is set to 2 minutes for the time metric, Step S2030 would consider whether or not the extrapolated value for the time metric exceeds the value of the time metric plus the failure threshold (2 minutes) for all of the probabilistic states identifying the upcoming node. If at least one of the probabilistic states passes this assessment, the recommended action may be selected as described above from one of those probabilistic states that passes this assessment. If the extrapolated value exceed the value of the time metric and/or efficiency metric for all probabilistic value identifying the upcoming node, a failure action may be performed rather than proceeding to Step S2040. The failure action may be to communicate to the driver and/or vehicle that the destination objectives can no longer be met (for example, as described in part A.4 above). A compromise of destination objectives may be offered to the user and a new ‘global’ policy determined based on those compromised objectives from the current location to the destination location (as explained in part A.4 above).”).
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 the method of Yang in view of Seegmiller in view of Subramanian to include associating each of the two or more objectives with a semantic label that indicates route preferences of the user; and providing the semantic label to the lane-level route planner to plan each of the lane-level contingency route, wherein a slack variable , related to a preference ordering of the two or more objectives, is provided with each of the lane-level contingency routes so that the lane-level contingency route is associated with the two or more objectives of the slack variable is selected, as taught by Kelly as disclosed above, in order to ensure optimal route selection (Kelly Paragraph 22 “According to a first aspect of the disclosure, there is provided a method of determining a journey guidance policy for a journey between a first location and a second location. The method comprises obtaining one or more objectives and determining a journey guidance policy based on the one or more objective”)
With respect to claim 3, and similarly claim 30, Yang in view of Seegmiller in view of Subramanian in view of Kelly teach identifying a failure to control the AV to make the lane change from the first lane segment to the second lane segment and in response to a failure to control the AV to make the lane change from the first lane segment to the second lane segment, controlling the AV according to the contingency route, wherein the first lane segment and the second lane segment are segments of different roads (See at least Seegmiller FIG. 5B and Paragraph 50).
With respect to claim 6, and similarly claim 32 and 38, Yang in view of Seegmiller in view of Subramanian in view of Kelly teach that the two or more objectives comprise at least one of a first objective relating to time, a second objective relating to autonomy, or a third objective relating to urban lane segments (See at least Subramanian Paragraph 29 “The vehicle computing system can generate potential trajectories for the autonomous vehicle to follow as it traverses the route. Each potential trajectory can be executable by the autonomous vehicle (e.g., feasible for the vehicle control systems to implement, etc.) and can include a one or more future coordinates, points, speeds, etc. over a specific amount of travel time (e.g., eight seconds, etc.). As described herein, the autonomous vehicle can select and implement a trajectory for the autonomous vehicle to navigate a specific segment of the route. For instance, the trajectory can be translated and provided to the vehicle control system(s) that can generate specific control signals for the autonomous vehicle (e.g., adjust steering, braking, velocity, and so on). The specific control signals can cause the autonomous vehicle to move in accordance with the selected trajectory”).
With respect to claim 29, Yang teaches an apparatus for route planning, comprising a processor, the processor configured to:
receiving a destination (Destination is inputted: See at least Yang Paragraph 9).;
obtaining or plan a lane-level route to the destination (A lane-level route is determined based on start point, destination, and map information: See at least Yang Paragraph 9),
and controlling the AV to traverse the lane-level route (Vehicle is controlled autonomously to travel the determined lane-level route: See at least Yang Paragraph 7).
Yang, however fails to explicitly disclose wherein the lane-level route is obtained based on a policy that uses two or more objectives specified by a user; obtaining or planning, with a lane-level route planner, according to the policy, locations along the lane-level route where an autonomous vehicle (AV) is to be controlled to make lane changes, prior to a planned lane change from a first lane segment to a second lane segment along the lane-level route, obtaining or planning, according to the a lane-level contingency route from the first lane segment to the destination in a case of a failure to control the AV to make the planned lane change from the first lane segment to the second lane segment; in response to the failure to control the AV to make the planned lane change from the first lane segment to the second lane segment, controlling the AV to traverse the lane-level contingency route from the first lane segment to the destination and associating each of the two or more objectives with a semantic label that indicates route preferences of the user; and providing the semantic label to the lane-level route planner to plan each of the lane-level contingency route, wherein a slack variable5, related toa preference ordering of the two or more objectives, is provided with each of the lane-level contingency routes so that the lane-level contingency route is associated with the two or more objectives of the slack variable is selected.
Seegmiller, however, teaches obtaining or planning, with a lane-level route planner, according to the policy, locations along the lane-level route where an autonomous vehicle (AV) is to be controlled to make lane changes, prior to a planned lane change from a first lane segment to a second lane segment along the lane-level route, obtaining or planning, according to the a lane-level contingency route from the first lane segment to the destination in a case of a failure to control the AV to make the planned lane change from the first lane segment to the second lane segment; in response to the failure to control the AV to make the planned lane change from the first lane segment to the second lane segment, controlling the AV to traverse the lane-level contingency route from the first lane segment to the destination (See at least Seegmiller FIG. 5B and Paragraph 50 “For example, as shown in FIG. 5B, the nominal route 500 of FIG. 5A may be expanded to include neighboring lane segments 521, 522, 514, 515, 516, and 517, as well as contingency lane segments 518 and 519 (in case the autonomous vehicle fails to make the lane change from lane 510 to 520), and contingency lane segments 528 and 529 (in case the autonomous vehicle fails to execute a turn by taking lane segment 530 through the intersection 540). Such contingency extensions to corridors 510 and 520 enable the autonomous vehicle to remain on the route in situations when it fails to make a required lane change or turn, until it is successfully re-routed. Lane changes are permissible from lane segments 511 to 521, lane segments 513 and/or 514 to 523 and/or 524, and lane segments 516 and/or 517 to 526 and/or 527. The expanded route representation may also include lane segments in intersections (512 to 522, 515 to 525, and 518 to 528), where lane change is not permitted.” | Paragraphs 68-69 “The system may then compute (333) dynamic costs associated with each of the candidate trajectories and identify (334) the candidate trajectory that has the minimum dynamic cost as the vehicle trajectory to be used for traversing the local region. The dynamic cost may be computed based on for example, the real-time perception information of the autonomous vehicle, location and predicted trajectories of objects in the autonomous vehicle's environment, locations of intersections in the corridors, passenger safety and comfort, location of permissible lane change regions in the corridors, the lane change location of that reference trajectory, static costs associated with various lane segments, the optimal route, contingency lane segments, or the like. For example, if a reference trajectory will take the autonomous vehicle into the path of another vehicle (based on the other vehicle's predicted trajectory), such a trajectory may be discarded as being too costly to execute. Similarly, a trajectory that requires the autonomous vehicle to make jerky movements during lane change may be discarded by assigning it a higher cost with respect to passenger comfort. In another example, the presence of another vehicle in a permissible lane change region in FIG. 5C may increase the cost of a lane change trajectory and the system may select the stay in lane trajectory as the best trajectory for traversing the local region of FIG. 5C. The stay-in-lane trajectory 558 for local region 550 shown in FIG. 5C, and the stay-in-lane trajectory 568 for local region 560 is shown in FIG. 5D. Similarly, two of the possible lane change trajectories 559(a) and 559(b) are shown in FIG. 5D.”).
It would have been obvious to one of ordinary skill in the art to have modified the method of Yang to include obtaining or planning, with a lane-level route planner, according to the policy, locations along the lane-level route where an autonomous vehicle (AV) is to be controlled to make lane changes, prior to a planned lane change from a first lane segment to a second lane segment along the lane-level route, obtaining or planning, according to the a lane-level contingency route from the first lane segment to the destination in a case of a failure to control the AV to make the planned lane change from the first lane segment to the second lane segment; in response to the failure to control the AV to make the planned lane change from the first lane segment to the second lane segment, controlling the AV to traverse the lane-level contingency route from the first lane segment to the destination, as taught by Seegmiller as disclosed above, in order to ensure continuous autonomous travel (Seegmiller Abstract “A method and a system for maneuvering an autonomous vehicle is disclosed. ”).
Yang in view of Seegmiller fail to explicitly disclose wherein the lane-level route is obtained based on a policy that uses two or more objectives specified by a user and associating each of the two or more objectives with a semantic label that indicates route preferences of the user; and providing the semantic label to the lane-level route planner to plan each of the lane-level contingency route, wherein a slack variable6, related to a preference ordering of the two or more objectives, is provided with each of the lane-level contingency routes so that the lane-level contingency route is associated with the two or more objectives of the slack variable is selected.
Subramanian teaches wherein the lane-level route is obtained based on a policy that uses two or more objectives specified by a user (See at least Subramanian Paragraph 37 “The vehicle computing system can generate one or more respective cost(s) for each respective trajectory in the plurality of trajectories based, at least in part, on the obtained sensor data. For instance, the vehicle computing system can score each trajectory against one or more cost function(s) designed to score a trajectory based on one or more motion planning criteria associated with the safe, efficient, and comfortable motion of a vehicle. The cost function(s), for example, can be encoded for the avoidance of object interferences, for the autonomous vehicle to stay on the travel way/within lane boundaries, prefer 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.). The cost function(s) can also, or alternatively, take into account at least one of the following object cost(s): interference costs (e.g., cost of avoiding/experiencing potential interference with another object, minimization of speed, etc.)” | Paragraph 58 “In some implementations, the verification system can obtain user input indicative of a costing change to at least one of the plurality of sub-cost functions and/or the one or more relational propositions. In such a case, the verification system can generate the verification data based on the costing change. For example, the costing change can include an additional sub-cost function in addition to the plurality of sub-cost functions. The verification system can generate the one or more cost modifications for the plurality of sub-cost functions based on the additional sub-cost function. As another example, the costing change can include an additional relational proposition in addition to the one or more relational propositions. The verification system can generate the one or more cost modifications for the plurality of sub-cost functions based on the additional relational proposition.”).
Subramanian teaches that the lane-level route is obtained using two or more objectives specified by a user to improve safety, efficiency, and a “comfortable motion of a vehicle” such that it would have been obvious to one of ordinary skill in the art before the effective filing date to have modified the method of Yang in view of Seegmiller to implement a preference for lane-level route using two or more objectives since the “cost functions” of Subramanian at least suggests the preference of “gentle accelerations to harsh ones”, “consider … speed limits”, and the “minimization of speeds”, which in turn improves comfort (Subramanian Paragraph 37 “the vehicle computing system can score each trajectory against one or more cost function(s) designed to score a trajectory based on one or more motion planning criteria associated with the safe, efficient, and comfortable motion of a vehicle. The cost function(s), for example, can be encoded for the avoidance of object interferences, for the autonomous vehicle to stay on the travel way/within lane boundaries, prefer 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.). The cost function(s) can also, or alternatively, take into account at least one of the following object cost(s): interference costs (e.g., cost of avoiding/experiencing potential interference with another object, minimization of speed, etc.)”.).
Yang in view of Seegmiller in view of Subramanian fail to explicitly disclose associating each of the two or more objectives with a semantic label that indicates route preferences of the user; and providing the semantic label to the lane-level route planner to plan each of the lane-level contingency route, wherein a slack variable7, related to a preference ordering of the two or more objectives, is provided with each of the lane-level contingency routes so that the lane-level contingency route is associated with the two or more objectives of the slack variable is selected.
Kelly teaches associating each of the two or more objectives with a semantic label that indicates route preferences of the user; and providing the semantic label to the lane-level route planner to plan each of the lane-level contingency route, wherein a slack variable8, related to a preference ordering of the two or more objectives, is provided with each of the lane-level contingency routes so that the lane-level contingency route is associated with the two or more objectives of the slack variable is selected See at least Kelly FIGS. 3 and 11 and Paragraphs 306-307 “FIG. 3 shows a representation of example process steps that may be undertaken to determine the journey definition (part A in FIG. 2). The process steps represented in FIG. 3 comprise determining a nominal journey understanding (part A.1), obtaining journey objectives and destination objectives (part A.2) and development of a directed graph that is representative of routing options. Each of these process steps is described in detail below. The destination objectives are a particular type of end objective. Destination objectives relate to objectives to be achieved on arrival at the destination location of the ‘global’ journey. A different, but analogous, type of end objectives may relate to objectives to be achieved at the end of a ‘local’ journey.” | Paragraphs 347-348 “If confidence is low that one or many of the objectives can be met (for example, none of the routes in the directed graph of routing options are expected to meet one or more of the objectives, or the directed graph of routing options comprises an insufficient number of potential routes that should meet some or all of the objectives with sufficient certainty to satisfy a confidence threshold), the process may proceed to part A.4. In part A.4, the user may be informed that confidence in meeting the objectives is low (for example, by displaying a warning message on the screen), at which time the process may return to part A.2 where the user may adjust one or more of the objectives to improve confidence. Alternatively, the user may indicate that they wish to proceed anyway, in which case the process proceeds to completion of the journey definition.” | Paragraphs 458-459 “Optionally, in Step S2030, it may be considered whether or not the extrapolated value (or value of current time metric and/or efficiency metric in the case where the recommended action is selected on arrival at the upcoming node) exceeds time metric and/or efficiency metric in all of the probabilistic states identifying the upcoming node by more than a threshold amount. Exceeding the values of the time metric and/or efficiency in all of the probabilistic states identifying the upcoming node by more than a threshold amount may indicate that time and/or efficiency for the journey so far are progressing poorly, thereby jeopardising the achievement of the destination objectives. The threshold amount may be set to any value, for example 0 minutes, 1 minute, 2 minutes, 4.3 minutes, 0 litres/gallons, 0.1 litres, 3 mpg, 0 kWh, 0.5 kWh, 0.8 kWh, etc). For example, if it is set to 2 minutes for the time metric, Step S2030 would consider whether or not the extrapolated value for the time metric exceeds the value of the time metric plus the failure threshold (2 minutes) for all of the probabilistic states identifying the upcoming node. If at least one of the probabilistic states passes this assessment, the recommended action may be selected as described above from one of those probabilistic states that passes this assessment. If the extrapolated value exceed the value of the time metric and/or efficiency metric for all probabilistic value identifying the upcoming node, a failure action may be performed rather than proceeding to Step S2040. The failure action may be to communicate to the driver and/or vehicle that the destination objectives can no longer be met (for example, as described in part A.4 above). A compromise of destination objectives may be offered to the user and a new ‘global’ policy determined based on those compromised objectives from the current location to the destination location (as explained in part A.4 above).”).
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 the method of Yang in view of Seegmiller in view of Subramanian to include associating each of the two or more objectives with a semantic label that indicates route preferences of the user; and providing the semantic label to the lane-level route planner to plan each of the lane-level contingency route, wherein a slack variable , related to a preference ordering of the two or more objectives, is provided with each of the lane-level contingency routes so that the lane-level contingency route is associated with the two or more objectives of the slack variable is selected, as taught by Kelly as disclosed above, in order to ensure optimal route selection (Kelly Paragraph 22 “According to a first aspect of the disclosure, there is provided a method of determining a journey guidance policy for a journey between a first location and a second location. The method comprises obtaining one or more objectives and determining a journey guidance policy based on the one or more objective”)
With respect to claim 36, Yang teaches a non-transitory computer-readable storage medium, comprising executable instructions that, when executed by a processor, facilitate performance of operations, comprising:
obtaining or planning a lane-level route to the destination using a map (A lane-level route is determined based on start point, destination, and map information: See at least Yang Paragraph 9),
and controlling the AV to traverse the lane-level route (Vehicle is controlled autonomously to travel the determined lane-level route: See at least Yang Paragraph 7).
Yang, however fails to explicitly disclose wherein the lane-level route is obtained based on a policy that uses two or more objectives specified by a user; obtaining or planning, with a lane-level route planner, according to the policy, locations along the lane-level route where an autonomous vehicle (AV) is to be controlled to make lane changes, prior to a planned lane change from a first lane segment to a second lane segment along the lane-level route, obtaining or planning, according to the a lane-level contingency route from the first lane segment to the destination in a case of a failure to control the AV to make the planned lane change from the first lane segment to the second lane segment; in response to the failure to control the AV to make the planned lane change from the first lane segment to the second lane segment, controlling the AV to traverse the lane-level contingency route from the first lane segment to the destination and associating each of the two or more objectives with a semantic label that indicates route preferences of the user; and providing the semantic label to the lane-level route planner to plan each of the lane-level contingency route, wherein a slack variable9, related toa preference ordering of the two or more objectives, is provided with each of the lane-level contingency routes so that the lane-level contingency route is associated with the two or more objectives of the slack variable is selected.
Seegmiller, however, teaches obtaining or planning, with a lane-level route planner, according to the policy, locations along the lane-level route where an autonomous vehicle (AV) is to be controlled to make lane changes, prior to a planned lane change from a first lane segment to a second lane segment along the lane-level route, obtaining or planning, according to the a lane-level contingency route from the first lane segment to the destination in a case of a failure to control the AV to make the planned lane change from the first lane segment to the second lane segment; in response to the failure to control the AV to make the planned lane change from the first lane segment to the second lane segment, controlling the AV to traverse the lane-level contingency route from the first lane segment to the destination (See at least Seegmiller FIG. 5B and Paragraph 50 “For example, as shown in FIG. 5B, the nominal route 500 of FIG. 5A may be expanded to include neighboring lane segments 521, 522, 514, 515, 516, and 517, as well as contingency lane segments 518 and 519 (in case the autonomous vehicle fails to make the lane change from lane 510 to 520), and contingency lane segments 528 and 529 (in case the autonomous vehicle fails to execute a turn by taking lane segment 530 through the intersection 540). Such contingency extensions to corridors 510 and 520 enable the autonomous vehicle to remain on the route in situations when it fails to make a required lane change or turn, until it is successfully re-routed. Lane changes are permissible from lane segments 511 to 521, lane segments 513 and/or 514 to 523 and/or 524, and lane segments 516 and/or 517 to 526 and/or 527. The expanded route representation may also include lane segments in intersections (512 to 522, 515 to 525, and 518 to 528), where lane change is not permitted.” | Paragraphs 68-69 “The system may then compute (333) dynamic costs associated with each of the candidate trajectories and identify (334) the candidate trajectory that has the minimum dynamic cost as the vehicle trajectory to be used for traversing the local region. The dynamic cost may be computed based on for example, the real-time perception information of the autonomous vehicle, location and predicted trajectories of objects in the autonomous vehicle's environment, locations of intersections in the corridors, passenger safety and comfort, location of permissible lane change regions in the corridors, the lane change location of that reference trajectory, static costs associated with various lane segments, the optimal route, contingency lane segments, or the like. For example, if a reference trajectory will take the autonomous vehicle into the path of another vehicle (based on the other vehicle's predicted trajectory), such a trajectory may be discarded as being too costly to execute. Similarly, a trajectory that requires the autonomous vehicle to make jerky movements during lane change may be discarded by assigning it a higher cost with respect to passenger comfort. In another example, the presence of another vehicle in a permissible lane change region in FIG. 5C may increase the cost of a lane change trajectory and the system may select the stay in lane trajectory as the best trajectory for traversing the local region of FIG. 5C. The stay-in-lane trajectory 558 for local region 550 shown in FIG. 5C, and the stay-in-lane trajectory 568 for local region 560 is shown in FIG. 5D. Similarly, two of the possible lane change trajectories 559(a) and 559(b) are shown in FIG. 5D.”).
It would have been obvious to one of ordinary skill in the art to have modified the method of Yang to include obtaining or planning, with a lane-level route planner, according to the policy, locations along the lane-level route where an autonomous vehicle (AV) is to be controlled to make lane changes, prior to a planned lane change from a first lane segment to a second lane segment along the lane-level route, obtaining or planning, according to the a lane-level contingency route from the first lane segment to the destination in a case of a failure to control the AV to make the planned lane change from the first lane segment to the second lane segment; in response to the failure to control the AV to make the planned lane change from the first lane segment to the second lane segment, controlling the AV to traverse the lane-level contingency route from the first lane segment to the destination, as taught by Seegmiller as disclosed above, in order to ensure continuous autonomous travel (Seegmiller Abstract “A method and a system for maneuvering an autonomous vehicle is disclosed. ”).
Yang in view of Seegmiller fail to explicitly disclose wherein the lane-level route is obtained based on a policy that uses two or more objectives specified by a user and associating each of the two or more objectives with a semantic label that indicates route preferences of the user; and providing the semantic label to the lane-level route planner to plan each of the lane-level contingency route, wherein a slack variable10, related to a preference ordering of the two or more objectives, is provided with each of the lane-level contingency routes so that the lane-level contingency route is associated with the two or more objectives of the slack variable is selected.
Subramanian teaches wherein the lane-level route is obtained based on a policy that uses two or more objectives specified by a user (See at least Subramanian Paragraph 37 “The vehicle computing system can generate one or more respective cost(s) for each respective trajectory in the plurality of trajectories based, at least in part, on the obtained sensor data. For instance, the vehicle computing system can score each trajectory against one or more cost function(s) designed to score a trajectory based on one or more motion planning criteria associated with the safe, efficient, and comfortable motion of a vehicle. The cost function(s), for example, can be encoded for the avoidance of object interferences, for the autonomous vehicle to stay on the travel way/within lane boundaries, prefer 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.). The cost function(s) can also, or alternatively, take into account at least one of the following object cost(s): interference costs (e.g., cost of avoiding/experiencing potential interference with another object, minimization of speed, etc.)” | Paragraph 58 “In some implementations, the verification system can obtain user input indicative of a costing change to at least one of the plurality of sub-cost functions and/or the one or more relational propositions. In such a case, the verification system can generate the verification data based on the costing change. For example, the costing change can include an additional sub-cost function in addition to the plurality of sub-cost functions. The verification system can generate the one or more cost modifications for the plurality of sub-cost functions based on the additional sub-cost function. As another example, the costing change can include an additional relational proposition in addition to the one or more relational propositions. The verification system can generate the one or more cost modifications for the plurality of sub-cost functions based on the additional relational proposition.”).
Subramanian teaches that the lane-level route is obtained using two or more objectives specified by a user to improve safety, efficiency, and a “comfortable motion of a vehicle” such that it would have been obvious to one of ordinary skill in the art before the effective filing date to have modified the method of Yang in view of Seegmiller to implement a preference for lane-level route using two or more objectives since the “cost functions” of Subramanian at least suggests the preference of “gentle accelerations to harsh ones”, “consider … speed limits”, and the “minimization of speeds”, which in turn improves comfort (Subramanian Paragraph 37 “the vehicle computing system can score each trajectory against one or more cost function(s) designed to score a trajectory based on one or more motion planning criteria associated with the safe, efficient, and comfortable motion of a vehicle. The cost function(s), for example, can be encoded for the avoidance of object interferences, for the autonomous vehicle to stay on the travel way/within lane boundaries, prefer 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.). The cost function(s) can also, or alternatively, take into account at least one of the following object cost(s): interference costs (e.g., cost of avoiding/experiencing potential interference with another object, minimization of speed, etc.)”.).
Yang in view of Seegmiller in view of Subramanian fail to explicitly disclose associating each of the two or more objectives with a semantic label that indicates route preferences of the user; and providing the semantic label to the lane-level route planner to plan each of the lane-level contingency route, wherein a slack variable11, related to a preference ordering of the two or more objectives, is provided with each of the lane-level contingency routes so that the lane-level contingency route is associated with the two or more objectives of the slack variable is selected.
Kelly teaches associating each of the two or more objectives with a semantic label that indicates route preferences of the user; and providing the semantic label to the lane-level route planner to plan each of the lane-level contingency route, wherein a slack variable12, related to a preference ordering of the two or more objectives, is provided with each of the lane-level contingency routes so that the lane-level contingency route is associated with the two or more objectives of the slack variable is selected See at least Kelly FIGS. 3 and 11 and Paragraphs 306-307 “FIG. 3 shows a representation of example process steps that may be undertaken to determine the journey definition (part A in FIG. 2). The process steps represented in FIG. 3 comprise determining a nominal journey understanding (part A.1), obtaining journey objectives and destination objectives (part A.2) and development of a directed graph that is representative of routing options. Each of these process steps is described in detail below. The destination objectives are a particular type of end objective. Destination objectives relate to objectives to be achieved on arrival at the destination location of the ‘global’ journey. A different, but analogous, type of end objectives may relate to objectives to be achieved at the end of a ‘local’ journey.” | Paragraphs 347-348 “If confidence is low that one or many of the objectives can be met (for example, none of the routes in the directed graph of routing options are expected to meet one or more of the objectives, or the directed graph of routing options comprises an insufficient number of potential routes that should meet some or all of the objectives with sufficient certainty to satisfy a confidence threshold), the process may proceed to part A.4. In part A.4, the user may be informed that confidence in meeting the objectives is low (for example, by displaying a warning message on the screen), at which time the process may return to part A.2 where the user may adjust one or more of the objectives to improve confidence. Alternatively, the user may indicate that they wish to proceed anyway, in which case the process proceeds to completion of the journey definition.” | Paragraphs 458-459 “Optionally, in Step S2030, it may be considered whether or not the extrapolated value (or value of current time metric and/or efficiency metric in the case where the recommended action is selected on arrival at the upcoming node) exceeds time metric and/or efficiency metric in all of the probabilistic states identifying the upcoming node by more than a threshold amount. Exceeding the values of the time metric and/or efficiency in all of the probabilistic states identifying the upcoming node by more than a threshold amount may indicate that time and/or efficiency for the journey so far are progressing poorly, thereby jeopardising the achievement of the destination objectives. The threshold amount may be set to any value, for example 0 minutes, 1 minute, 2 minutes, 4.3 minutes, 0 litres/gallons, 0.1 litres, 3 mpg, 0 kWh, 0.5 kWh, 0.8 kWh, etc). For example, if it is set to 2 minutes for the time metric, Step S2030 would consider whether or not the extrapolated value for the time metric exceeds the value of the time metric plus the failure threshold (2 minutes) for all of the probabilistic states identifying the upcoming node. If at least one of the probabilistic states passes this assessment, the recommended action may be selected as described above from one of those probabilistic states that passes this assessment. If the extrapolated value exceed the value of the time metric and/or efficiency metric for all probabilistic value identifying the upcoming node, a failure action may be performed rather than proceeding to Step S2040. The failure action may be to communicate to the driver and/or vehicle that the destination objectives can no longer be met (for example, as described in part A.4 above). A compromise of destination objectives may be offered to the user and a new ‘global’ policy determined based on those compromised objectives from the current location to the destination location (as explained in part A.4 above).”).
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 the method of Yang in view of Seegmiller in view of Subramanian to include associating each of the two or more objectives with a semantic label that indicates route preferences of the user; and providing the semantic label to the lane-level route planner to plan each of the lane-level contingency route, wherein a slack variable , related to a preference ordering of the two or more objectives, is provided with each of the lane-level contingency routes so that the lane-level contingency route is associated with the two or more objectives of the slack variable is selected, as taught by Kelly as disclosed above, in order to ensure optimal route selection (Kelly Paragraph 22 “According to a first aspect of the disclosure, there is provided a method of determining a journey guidance policy for a journey between a first location and a second location. The method comprises obtaining one or more objectives and determining a journey guidance policy based on the one or more objective”)
With respect to claim 39, Yang in view of Seegmiller in view of Subramanian in view of Kelly teach specifying, via a route planner, preferences related to road types so that a preferred road type is selected over other road types when planning the lane-level route See at least Urmson FIGS. 4-5 and Cols. 1-2 “The autonomous vehicle may monitor the vehicle's movement, and collect data Such as speed, lane changes, and changes in space between vehicles in front of and behind the driver. The vehicle may also collect data such as (but not limited to) gas usage and application of throttles/brakes. Various driver preferences may also be recorded or derived from the collected data. Driver preferences may include a preference of windy roads over Straight roads, right turns over left turns, multi-lane highways over side roads, etc. From data collected over time, the vehicle may learn the driver's driving pattern and gauge driver preferences. Various machine learn ing algorithms may be used to facilitate the learning process. The learning period may depend on various factors such as the specific driving feature that the vehicle tries to learn (e.g., it may take a shorter time to learn a preferred speed of acceleration than a preference of multi-lane highways over side roads). When the vehicle learns the driving patterns, the vehicle may perform driver-specific autonomous driving when it identifies which driver is currently in the driver's seat (e.g., changing lanes more often to move faster for driver A than for driver B, selecting a route with more windy roads for driver C. and selecting a route with more straight roads for driver D). The machine-learned driving pattern”).
Claims 5, 31, and 37 are rejected under 35 U.S.C. 103 as being unpatentable over Yang (US 20180273032 A1) (“Yang”) in view of Seegmiller (US 20210107566 A1) (“Seegmiller”) in view of Subramanian (US 20220176995 A1) (“Subramanian”) in view of Kelly (US 20190346275 B1) (“Kelly”) further in view of Kevin (GB 2586490 A) (“Kevin”) (Translation Attached).
With respect to claim 5, Yang in view of Seegmiller in view of Subramanian in view of Kelly teach a comfort objective that indicates a preference for lane segments where traffic moves at speeds within a threshold speed of respective speed limits on the lane segments (See at least Subramanian Paragraph 37).
Yang in view of Seegmiller in view of Subramanian in view of Kelly fail to explicitly disclose that the first lane segment or the second lane segment is associated with respective metadata relating to the comfort objective and that the respective metadata relating to the comfort objective are learned from historical driving patterns.
Kevin, however, teaches that the first lane segment or the second lane segment is associated with respective metadata relating to the comfort objective (See at least Kevin Page 5 lines 27-33 “The method includes analysing a first route where the selected style data is not known, then searching a database of routes to identify routes with similar route metadata to the first route, creating a matching driving style for the first route. If a matching route to the first route is found then the method further includes the step of using parameters from the matching route for creating style parameters for the first route.” | Page 17 line 38 – Page 18 line 2 “Therefore, in a first case the recorded data may be used to simply replay how a route was previously driven. For example, it was recorded that that for segment "a" of route AB that the car was driven with a max speed of 65kph on a single carriageway in the UK, close to allowed speed limit. This may be classed as a "comfort" style of driving on this type of road, and if the driver choosing "comfort" style" for the same route, then the system will search for and find the previously driven route that matched comfort parameters and use that recorded data to pilot the car.”)
and that the respective metadata relating to the comfort objective are learned from historical driving patterns (See at least Kevin Page 3 “The parameters defining the selected driving style may be derived from previously recorded parameters. The previously recorded parameters can be derived from previous journeys driven by a vehicle on the selected route.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have modified the method of Yang in view of Seegmiller in view of Subramanian in view of Kelly so that the first lane segment or the second lane segment is associated with respective metadata relating to the comfort objective and that the respective metadata relating to the comfort objective are learned from historical driving patterns, as taught by Kevin as disclosed above, in order to ensure accurate information for each segment is provided based on various parameters (Kevin Page 1 lines 5-8 “The present invention relates to a method and system for controlling a vehicle according to driver preferences. In particular, the invention relates to a method and system for controlling autonomous vehicles according to driver preferences”).
With respect to claim 31, and similarly claim 37, Yang in view of Seegmiller in view of Subramanian in view of Kelly teach a comfort objective that indicates a preference for lane segments where traffic moves at speeds within a threshold speed of respective speed limits on the lane segments (See at least Subramanian Paragraph 37)
Yang in view of Seegmiller in view of Subramanian in view of Kelly fail to explicitly disclose that the first lane segment or the second lane segment is associated with respective metadata relating to the comfort objective and wherein the respective metadata are learned from historical driving patterns.
Kevin, however, teaches that the first lane segment or the second lane segment is associated with respective metadata relating to the comfort objective (See at least Kevin Page 5 lines 27-33 “The method includes analysing a first route where the selected style data is not known, then searching a database of routes to identify routes with similar route metadata to the first route, creating a matching driving style for the first route. If a matching route to the first route is found then the method further includes the step of using parameters from the matching route for creating style parameters for the first route.” | Page 17 line 38 – Page 18 line 2 “Therefore, in a first case the recorded data may be used to simply replay how a route was previously driven. For example, it was recorded that that for segment "a" of route AB that the car was driven with a max speed of 65kph on a single carriageway in the UK, close to allowed speed limit. This may be classed as a "comfort" style of driving on this type of road, and if the driver choosing "comfort" style" for the same route, then the system will search for and find the previously driven route that matched comfort parameters and use that recorded data to pilot the car.”)
and that the respective metadata relating to the comfort objective were learned from historical driving patterns (See at least Kevin Paragraph 3 lines 1-3 “The parameters defining the selected driving style may be derived from previously recorded parameters. The previously recorded parameters can be derived from previous journeys driven by a vehicle on the selected route” | Page 13 lines 23-27 “For example, if a driver has already driven the route AB, the ADS 20 can use the data previously gathered on that driving route to replicate the original driving experience by simply replaying and following the location/ speed, and other control data. Alternatively, this route can be experienced by another driver using the system to access the route driving style data”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to have modified the method of Yang in view of Seegmiller in view of Subramanian in view of Kelly so that the first lane segment or the second lane segment is associated with respective metadata relating to the comfort objective and wherein the respective metadata are learned from historical driving patterns, as taught by Kevin as disclosed above, in order to ensure accurate information for each segment is provided based on various parameters (Kevin Page 1 lines 5-8 “The present invention relates to a method and system for controlling a vehicle according to driver preferences. In particular, the invention relates to a method and system for controlling autonomous vehicles according to driver preferences”).
Claims 7 and 33 rejected under 35 U.S.C. 103 as being unpatentable over Yang (US 20180273032 A1) (“Yang”) in view of Seegmiller (US 20210107566 A1) (“Seegmiller”) in view of Subramanian (US 20220176995 A1) (“Subramanian”) in view of Kelly (US 20190346275 B1) (“Kelly”) further in view of Weiskircher (“Predictive Guidance and Control Framework for (Semi-)Autonomous Vehicles in Public Traffic”) (“Weiskircher”) (Attached).
With respect to claim 7, and similarly claim 33, Yang in view of Seegmiller in view of Subramanian in view of Kelly teach determining routes based on vehicle objectives (Routes are determined based on user needs or objectives: See at least Subramanian Paragraph 37) and that at least one of the two or more objectives is set by the user (See at least Subramanian Paragraph 58).
Yang in view of Seegmiller in view of Subramanian in view of Kelly fail to explicitly disclose that the two or more objectives are related in a preference ordering and wherein the slack variable indicates a change in value based on at least one of the two or more objectives in association with the preference ordering.
Weiskircher, however, teaches that the two or more objectives are related in a preference ordering and wherein the slack variable indicates a change in value based on at least one of the two or more objectives in association with the preference ordering (See at least Weiskircher Section A “Herein, the first constraint in (21a) is intended to restrict the input accelerations at,d and an,d [an,d is derived from (9) and (14)] to the physical ability of the tire/road contact patch. This is represented by the elliptical region with half minor/major axes μHg−ζgg and an,gg(μHg−ζgg) in Fig. 6. μH is the maximum friction coefficient of the tire/road contact patch. The normalization coefficient an,gg≤1 provides a parameterization for tuning the lateral acceleration limit. The slack variable ζgg and its upper limit ζ¯¯¯gg were introduced in [20]. These make it possible to lower the vehicle accelerations for the sake of more comfortable trajectories except in critical maneuvers, where the slack variable allows full use of the acceleration potential. The slack variable ζgg is formulated to track a desired value ζgg,r and the two norm of the tracking error ζgg−ζgg,r is weighted in the objective function of the MPC in the PTG level. This formulation is often called a soft constraint. Furthermore, the constraints limit the control inputs to the physical potential of the vehicle from actuation perspectives by adding limits for braking a––t,d , traction a¯¯¯t,d , and steering a––n,d,a¯¯¯n,d . The hatched area in Fig. 6 shows the final available acceleration input space (hard constraints on an,d are omitted for clarity).” | Section E “As already mentioned, the selection of the control references in the objective function is one way to prioritize different fully autonomous and semiautonomous driving objectives. Another way is the selection of the weighting matrices Q and R in the objective function (34). For the fully autonomous driving, the objective of following the middle of a lane ye,r is combined with tracking a reference velocity vt,r using both the longitudinal and lateral control inputs. The reference velocity is obtained from the assigner module according to the perception of the speed limits or the passengers’ preference [23], [24]. Sometimes a deviation ye−ye,r≠0 may appear as an optimal tradeoff between speed and path tracking, accelerations (control), and other constraints. The slack weights Qsl,gg/Do are set high to generate comfortable trajectories by restricting the use of full acceleration potential except in safety-related situations, e.g., for CA. In those situations, the soft limit defined by ζgg and the distance to objects via ζDo are reduced automatically to generate feasible solutions in the presence of hard constraints like the distance to obstacle object s−soi .”).
It would have been obvious to one of ordinary skill in the art to have modified the method of Yang in view of Seegmiller in view of Subramanian in view of Kelly to include that the two or more objectives are related in a preference ordering and wherein the slack variable indicates a change in value based on at least one of the two or more objectives in association with the preference ordering, as taught by Weiskircher as disclosed above, in order to ensure optimal ordering of objectives based on user preferences (Weiskircher Abstract “In this paper, a predictive trajectory guidance and control framework is proposed that enables the safe operation of autonomous and semiautonomous vehicles considering the constraints of operating in dynamic public traffic”).
Claims 8 and 34 are rejected under 35 U.S.C. 103 as being unpatentable over Yang (US 20180273032 A1) (“Yang”) in view of Seegmiller (US 20210107566 A1) (“Seegmiller”) in view of Subramanian (US 20220176995 A1) (“Subramanian”) in view of Kelly (US 20190346275 B1) (“Kelly”) in view of Weiskircher (“Predictive Guidance and Control Framework for (Semi-)Autonomous Vehicles in Public Traffic”) (“Weiskircher”) (Attached) further in view of Xin (“Enable Faster and Smoother Spatio-Temporal Trajectory Planning for Autonomous Vehicles in Constrained Dynamic Environment”) (“Xin”) (Attached).
With respect to claim 8, and similarly claim 34, Yang in view of Seegmiller in view of Subramanian in view of Kelly in view of Weiskircher fail to explicitly disclose the use of a directed acyclic graph.
Xin, however, teaches the use of a directed acyclic graph (See at least Xin Introduction “Therefore, this paper proposes a novel hybrid planning framework composed of two modules, which preserves the strength of both graph-search-based methods for avoiding local optimality and optimization-based methods for local refinement, thus enabling faster and smoother spatio-temporal trajectory planning in constrained dynamic environment. The proposed method first constructs spatio-temporal driving space based on directed acyclic graph (DAG) and efficiently searches a spatio-temporal trajectory using the improved A* algorithm. Then taking the search result as reference, locally convex feasible driving area is designed and MPC is applied to further optimize the trajectory with a comprehensive consideration of vehicle kinematics and moving obstacles”).
It would have been obvious to one of ordinary skill in the art to have modified the method of Yang in view of Seegmiller in view of Subramanian in view of Kelly in view of Weiskircher to include the use of a directed acyclic graph, as taught by Xin, for the preference ordering, in order to ensure accurate ordering of objectives for a vehicle following a trajectory (Xin “To generate the smooth spatio-temporal trajectory efficiently in constrained dynamic environment, a hybrid planning method is proposed in this paper”).
Claims 9 and 35 rejected under 35 U.S.C. 103 as being unpatentable over Yang (US 20180273032 A1) (“Yang”) in view of Seegmiller (US 20210107566 A1) (“Seegmiller”) in view of Subramanian (US 20220176995 A1) (“Subramanian”) in view of Kelly (US 20190346275 B1) (“Kelly”) in view of Weiskircher (“Predictive Guidance and Control Framework for (Semi-)Autonomous Vehicles in Public Traffic”) (“Weiskircher”) (Attached) in view of Xin (“Enable Faster and Smoother Spatio-Temporal Trajectory Planning for Autonomous Vehicles in Constrained Dynamic Environment”) (“Xin”) (Attached) further in view of Altman (“Constrained Markov Decision Processes”) (“Altman”) (Attached).
With respect to claim 9, and similarly claim 35, Yang in view of Seegmiller in view of Subramanian in view of Kelly in view of Weiskircher in view of Xin fail to explicitly disclose the use of a fan preference ordering graph.
Altman, however, teaches the use of a fan preference ordering graph (See at least Altman Page 21 “The models that we study in this monograph are special in that more than one objective cost exists; the controller minimizes one of the objectives subject to constraints on the others. We shall call this class of MDPs Constrained MDPs, or simply CMDPs.” | Chapter 13 | Section 2.2 | Section 5.4 ).
As discussed by Altman, Constrained Markov Decision Processes, Finite Markov Decision Processes, and Infinite Markov Decision Processes, lane graphs, state lattice graphs, is well known in the art.
Therefore, it would have been obvious to try by one of ordinary skill in the art at the time of the invention was made, to pick a fan preference ordering graph and incorporate it into the system of Yan in view of Seegmiller in view of Subramanian in view of Kelly in view of Weiskircher in view of Xin since there are a finite number of identified, predictable potential solutions, i.e., graph types/ preference ordering (i.e. Constrained Markov Decision Processes, Finite Markov Decision Processes, and Infinite Markov Decision Processes, lane graphs, state lattice graphs) to the recognized need and one of ordinary skill in the art could have pursued the known potential solutions with a reasonable expectation of success in order to optimize conflation of the reward function and reduce computational complexity. In addition, Altman teaches that the benefits of incorporating a fan preference ordering graph / constrained choice graph is to fully predict the evolution of the state of the system as a result of applying a sequence of actions (Altman 2.1)
Claims 11 is rejected under 35 U.S.C. 103 as being unpatentable over Yang (US 20180273032 A1) (“Yang”) in view of Seegmiller (US 20210107566 A1) (“Seegmiller”) in view of Subramanian (US 20220176995 A1) (“Subramanian”) in view of Kelly (US 20190346275 B1) (“Kelly”) in view of Weiskircher (“Predictive Guidance and Control Framework for (Semi-)Autonomous Vehicles in Public Traffic”) (“Weiskircher”) (Attached) further in view of Kubil (“Modeling the Generalized Multi-Objective Vehicle Routing Problem Based on Costs”) (“Kubil”) (Attached).
With Respect to claim 11, Yang in view of Seegmiller in view of Subramanian in view of Kelly in view of Weiskircher teach determining a trajectory for a vehicle (A lane-level route is determined based on start point, destination, and map information: See at least Yang Paragraph 9).
Yang in view of Seegmiller in view of Subramanian in view of Kelly in view of Weiskircher fail to explicitly disclose the use of scalarization function of the one or more objectives.
Kubil, however teaches the use of scalarization function of the one or more objectives (See at least Kubil Introduction: “The purpose of the research is to design a mathematical model of multi-objective VRP and a scalarization approach for reducing decision-maker participation”).
It would have been obvious to one of ordinary skill in the art to have modified the method of Yang in view of Seegmiller in view of Subramanian in view of Kelly in view of Weiskircher to include the use of scalarization function of the one or more objectives, as taught by Kubil as disclosed above, in order to ensure accurate route determination (Kubil Abstract “The following article addresses a complex combinatorial optimization and integer-programming problem, referred to as the vehicle routing problem, which is typically related to the field of transportation logistics”).
Claim 40 is rejected under 35 U.S.C. 103 as being unpatentable over Yang (US 20180273032 A1) (“Yang”) in view of Seegmiller (US 20210107566 A1) (“Seegmiller”) in view of Subramanian (US 20220176995 A1) (“Subramanian”) in view of Kelly (US 20190346275 B1) (“Kelly”) further in view of Kalyanaraman (US 20160272078 A1) (“Kalyanaraman”).
With respect to claim 40, Yang in view of Seegmiller in view of Subramanian in view of Kelly fail to explicitly disclose balancing the two or more objectives at a same time, wherein the two or more objectives relate to battery efficiency and stop points for recharging the battery.
Kalyanaraman teaches balancing the two or more objectives at a same time, wherein the two or more objectives relate to battery efficiency and stop points for recharging the battery (See at least Kalyanaraman Paragraphs 24-25 “In the example embodiment, intelligent optimizer program 106 determines and selects the optimal route based on the overall commute time, including anticipated delays (step 216). In other embodiments, however, intelligent optimizer program 106 may select the route with the least mileage, greatest estimated battery efficiency, or some other route distinguishing factor decided by the user … Intelligent optimizer program 106 determines the optimal battery pack load required to power vehicle 116 to a destination and back, including any scheduled stops and anticipated delays (step 218). In determining the optimal battery pack load, intelligent optimizer program 106 takes into consideration travel distance before returning to battery exchanger 114, delays, load demand, temperature, elevation, battery pack weight, and other factors having an effect on the distance vehicle 116 can travel using battery power. In the example embodiment, an optional battery reserve charge of 20% is included, if available, in the event extra stops, unexpected delays, or detours are encountered. In other embodiments, the driver of vehicle 116 is free to allocate any percent battery reserve the user sees fit so long as the battery packs of battery packs 122 are available (i.e. the battery packs are not already in use or depleted). Continuing the example above where a user is driving to work in NYC and back, if intelligent optimizer program 106 determines that the commute of thirty (30) miles each way requires five (5) battery packs, intelligent optimizer program 106 will include an extra battery pack to account for unexpected delays, stops, or other events which prolong vehicle 116 returning to battery exchanger 114”).
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 the method of Yang in view of Seegmiller in view of Subramanian in view of Kelly to include balancing the two or more objectives at a same time, wherein the two or more objectives relate to battery efficiency and stop points for recharging the battery, as taught by Kalyanaraman as disclosed above, in order to ensure optimal battery levels of a vehicle (Kalyanaraman Paragraph 1 “The present invention relates generally to battery management, and more particularly to the division of battery packs to improve electric vehicle performance.”),
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IBRAHIM ABDOALATIF ALSOMAIRY whose telephone number is (571)272-5653. The examiner can normally be reached M-F 7:30-5:30.
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/IBRAHIM ABDOALATIF ALSOMAIRY/ Examiner, Art Unit 3667 /KENNETH J MALKOWSKI/Primary Examiner, Art Unit 3667
1 There is no limiting definition of what constitutes a “slack variable” however the specification states in paragraphs 80-81 “Each objective can have a non-negative slack δ:e→R+, describing how much the user is willing to “spend” in the value of one objective to improve the value of another. As such, the one or more objectives are related in a preference ordering including slack variables”
2 There is no limiting definition of what constitutes a “slack variable” however the specification states in paragraphs 80-81 “Each objective can have a non-negative slack δ:e→R+, describing how much the user is willing to “spend” in the value of one objective to improve the value of another. As such, the one or more objectives are related in a preference ordering including slack variables”
3 There is no limiting definition of what constitutes a “slack variable” however the specification states in paragraphs 80-81 “Each objective can have a non-negative slack δ:e→R+, describing how much the user is willing to “spend” in the value of one objective to improve the value of another. As such, the one or more objectives are related in a preference ordering including slack variables”
4 There is no limiting definition of what constitutes a “slack variable” however the specification states in paragraphs 80-81 “Each objective can have a non-negative slack δ:e→R+, describing how much the user is willing to “spend” in the value of one objective to improve the value of another. As such, the one or more objectives are related in a preference ordering including slack variables”
5 There is no limiting definition of what constitutes a “slack variable” however the specification states in paragraphs 80-81 “Each objective can have a non-negative slack δ:e→R+, describing how much the user is willing to “spend” in the value of one objective to improve the value of another. As such, the one or more objectives are related in a preference ordering including slack variables”
6 There is no limiting definition of what constitutes a “slack variable” however the specification states in paragraphs 80-81 “Each objective can have a non-negative slack δ:e→R+, describing how much the user is willing to “spend” in the value of one objective to improve the value of another. As such, the one or more objectives are related in a preference ordering including slack variables”
7 There is no limiting definition of what constitutes a “slack variable” however the specification states in paragraphs 80-81 “Each objective can have a non-negative slack δ:e→R+, describing how much the user is willing to “spend” in the value of one objective to improve the value of another. As such, the one or more objectives are related in a preference ordering including slack variables”
8 There is no limiting definition of what constitutes a “slack variable” however the specification states in paragraphs 80-81 “Each objective can have a non-negative slack δ:e→R+, describing how much the user is willing to “spend” in the value of one objective to improve the value of another. As such, the one or more objectives are related in a preference ordering including slack variables”
9 There is no limiting definition of what constitutes a “slack variable” however the specification states in paragraphs 80-81 “Each objective can have a non-negative slack δ:e→R+, describing how much the user is willing to “spend” in the value of one objective to improve the value of another. As such, the one or more objectives are related in a preference ordering including slack variables”
10 There is no limiting definition of what constitutes a “slack variable” however the specification states in paragraphs 80-81 “Each objective can have a non-negative slack δ:e→R+, describing how much the user is willing to “spend” in the value of one objective to improve the value of another. As such, the one or more objectives are related in a preference ordering including slack variables”
11 There is no limiting definition of what constitutes a “slack variable” however the specification states in paragraphs 80-81 “Each objective can have a non-negative slack δ:e→R+, describing how much the user is willing to “spend” in the value of one objective to improve the value of another. As such, the one or more objectives are related in a preference ordering including slack variables”
12 There is no limiting definition of what constitutes a “slack variable” however the specification states in paragraphs 80-81 “Each objective can have a non-negative slack δ:e→R+, describing how much the user is willing to “spend” in the value of one objective to improve the value of another. As such, the one or more objectives are related in a preference ordering including slack variables”
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