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 the Claims
This Office Action is in response to the Application filed on December 19, 2024. Claims 1-14 are presently pending and are presented for examination.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on July 3, 2025 and January 20, 2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 14 is objected to because of the following informalities: In regards to claim 14, the claim recites “the trajectory”, however this was not previously introduced and therefore should read --a trajectory--. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis - Step 1
Claims 1-12 recite a method/process, therefore claims 1-12 are within at least one of the four statutory categories.
Claims 13-14 recite an apparatus/machine, therefore claims 13-14 are within at least one of the four statutory categories.
101 Analysis - Step 2A, Prong 1
Regarding Prong 1 of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 includes limitations that recites mathematical concepts and/or mental processes (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
A computer-implemented method of determining a trajectory of an autonomous vehicle, the method comprising:
receiving a candidate path for the autonomous vehicle to travel, and positional information of one or more objects;
determining a plurality of passing strategies, each passing strategy comprising a passing action defining how the autonomous vehicle is constrained to pass the or each object;
ranking the plurality of passing strategies; and
determining a trajectory along the path for a highest ranked passing strategy.
These limitations, as drafted, is a system that, under its broadest reasonable interpretation, covers performance of the limitation as a mental process. That is, nothing in the claim elements preclude the steps from practically being performed as mental process. For example, " determining a plurality of pass strategies…", “ranking the plurality of passing strategies…” and " determining a trajectory..." encompass mental processes as a human can perform these limitations using observations, evaluations, judgments, and/or opinions. " determining a plurality of pass strategies…", and " determining a trajectory..."involves a human judging and/or evaluating possible passing strategies and determining the trajectory required for the vehicle to complete the selected passing strategy and “ranking the plurality of passing strategies…” involves a human making a judgment or using paper and pencil to determine a ranking of the passing strategies. Thus, the claim recites at least a mental process.
101 Analysis - Step 2A, Prong 2
Regarding Prong 2 of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract idea into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a "practical application."
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the "additional limitations" while the bolded portions continue to represent the "abstract idea"):
A computer-implemented method of determining a trajectory of an autonomous vehicle, the method comprising:
receiving a candidate path for the autonomous vehicle to travel, and positional information of one or more objects;
determining a plurality of passing strategies, each passing strategy comprising a passing action defining how the autonomous vehicle is constrained to pass the or each object;
ranking the plurality of passing strategies; and
determining a trajectory along the path for a highest ranked passing strategy.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitation of " A computer-implemented method of determining a trajectory of an autonomous vehicle” the examiner submits that this limitation characterizes the method as being associated with a trajectory of an autonomous vehicle, which merely amounts to indicating a field of use or technological environment in which to apply a judicial exception and cannot integrate the judicial exception into a practical application or amount to significantly more than the exception itself (see MPEP 2106.05(h)). Additionally, the claim limitation “receiving a candidate path for the autonomous vehicle to travel, and positional information …” does not amount to an inventive concept since it is insignificant extra-solution activity as it is merely a form of data collection and outputting (MPEP § 2106.05(g)). The examiner submits that these limitations are mere data collection and outputting components to apply the above-noted abstract idea within an indicated field of use (MPEP §2106.05).
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular process for safety performance evaluation, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis - Step 2B
Regarding Step 2B in the 2019 PEG, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “receiving a candidate path for the autonomous vehicle to travel, and positional information …" amounts to extra-solution data gathering and outputting. Additionally, the specification demonstrates the well-understood, routine, conventional nature of additional elements as it describes the additional elements as well-understood or routine or conventional (or an equivalent term), as a commercially available product, or in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. §112(a). With respect to “receiving a candidate path for the autonomous vehicle to travel, and positional information …" it was ruled within Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015), which are recited within MPEP 2106.05(d)(II) that mere data collection or receiving/obtaining and transmitting of data over a network is well-understood, routine, and conventional function when it is claimed in a merely generic matter, as it is here. Additionally, “A computer-implemented method of determining a trajectory of an autonomous vehicle” is merely a technological environment or field of use as the limitations merely link the use of a judicial exception to a particular technological environment or field of use (See MPEP 2106.05(h)).
Claim 13 recites analogous limitations to that of claim 1, and is therefore rejected by the same premise.
Claim 14 recites analogous limitations to that of claim 1, with the exception of a “one or more actuators configured to maneuver the autonomous vehicle…”, however, this merely amounts to indicating a field of use or technological environment in which to apply a judicial exception and cannot integrate the judicial exception into a practical application or amount to significantly more than the exception itself (see MPEP 2106.05(h)), and is therefore rejected by the same premise.
Dependent claims 2-12 specify limitations that elaborate on the abstract idea of claim 1, and thus are directed to an abstract idea nor do the claims recite additional limitations that integrate the claims into a practical application or amount to "significantly more" for similar reasons.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 11 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
In regards to claim 11, “the original of the polygon” does not appear to make sense, rendering the claim indefinite. “the original” was not previously found within the claims that the claim is dependent upon, nor was an explanation of this feature found within the specification, making it unclear what is being conveyed in the claim. For examination purposes, the claim is being interpreted as referring to the starting location of the vehicle.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-14 is/are rejected under 35 U.S.C. 102(a)(1) as being Seegmiller et al. (US 20210108936; hereinafter Seegmiller).
In regards to claim 1, Seegmiller discloses of a computer-implemented method of determining a trajectory of an autonomous vehicle (“Systems and methods of maneuvering an autonomous vehicle in a local region using topological planning, while traversing a route to a destination location, are disclosed. The system includes an autonomous vehicle including one or more sensors and a processor. The processor is configured to determine the local region on the route and receive real-time information corresponding to the local region. The processor performs topological planning to identify on or more topologically distinct classes of trajectories, compute a constraint set for each of the one or more topologically distinct classes of trajectories, optimize a trajectory to generate a candidate trajectory for each constraint set, and select a trajectory for the autonomous vehicle to traverse the local region from amongst the one or more candidate trajectories. Each of the one or more topologically distinct classes is associated with a plurality of trajectories that take the same combination of discrete actions with respect to objects in the local region.” (Abstract)), the method comprising:
receiving a candidate path for the autonomous vehicle to travel, and positional information of one or more objects (“To generate the nominal route, the system may first identify candidate routes that a vehicle can travel on to get from the start position to the destination. The system may then score the candidate routes and identify a nominal route to reach the destination. For example, the system may generate a nominal route that minimizes Euclidean distance traveled or other cost function during the route. Depending on implementation, the system may generate one or more routes using various routing methods, such as Dijkstra's algorithm, Bellman-Ford algorithm, or other algorithms. The routing module 112(b) may also use the traffic information to generate a navigation route that reflects expected conditions of the route (e.g., current day of the week or current time of day, etc.), such that a route generated for travel during rush-hour may differ from a route generated for travel late at night.” (Para 0044), “At 348, the system may optimize a trajectory for each constraint set to determine a candidate trajectory for each topologically distinct class. This optimization may be performed using model-predictive control or another algorithm, to generate a dynamically feasible and comfortable trajectory that satisfies the constraint set. Computation of the constraint set takes into account real-time information such as, without limitation, perception information about the local region (e.g. traffic signal state) and the predicted trajectories of other objects (e.g. vehicles, pedestrians). In some embodiments, the predicted trajectories may be updated in trajectory optimization to model interaction with the autonomous vehicle. For example, a vehicle in a lane that the autonomous vehicle intends to merge into may decelerate to widen the gap, and the optimized trajectory(ies) may be updated accordingly.” (Para 0069), “The system may perform topologically planning 344 to determine one or more topologically distinct classes in the local region (based on an analysis of the received real-time information). Each topologically distinct class is associated with a set of trajectories that take the same discrete actions with respect to objects in the local region. For example, as shown in FIG. 4A, for traveling a local region 400 between a start position 410 (i.e., the current position of the autonomous vehicle) and a goal position 420 (when the autonomous vehicle will exit the local region), an autonomous vehicle will need to avoid the objects 401, 402, 403. The system may generate the topologically distinct classes by analyzing the spatial relationships the autonomous vehicle can have with the objects 401, 402, 403 with respect to a reference path 405. For the example shown in FIG. 4A, the autonomous vehicle may avoid each of the objects 401, 402, 403 by tracking behind (a longitudinal action), or by passing the object either on the left side or on the right side (a lateral action). The various options are shown as a tree graph 430 in FIG. 4B. Each path 408a-o through the tree graph 430 defines a topologically distinct class of trajectories.” (Para 0050));
determining a plurality of passing strategies, each passing strategy comprising a passing action defining how the autonomous vehicle is constrained to pass the or each object (“The system may perform topologically planning 344 to determine one or more topologically distinct classes in the local region (based on an analysis of the received real-time information). Each topologically distinct class is associated with a set of trajectories that take the same discrete actions with respect to objects in the local region. For example, as shown in FIG. 4A, for traveling a local region 400 between a start position 410 (i.e., the current position of the autonomous vehicle) and a goal position 420 (when the autonomous vehicle will exit the local region), an autonomous vehicle will need to avoid the objects 401, 402, 403. The system may generate the topologically distinct classes by analyzing the spatial relationships the autonomous vehicle can have with the objects 401, 402, 403 with respect to a reference path 405. For the example shown in FIG. 4A, the autonomous vehicle may avoid each of the objects 401, 402, 403 by tracking behind (a longitudinal action), or by passing the object either on the left side or on the right side (a lateral action). The various options are shown as a tree graph 430 in FIG. 4B. Each path 408a-o through the tree graph 430 defines a topologically distinct class of trajectories.” (Para 0050), “A constraint set may be infeasible based on the incompatibility of the state of the autonomous vehicle and a constraint. For example, as illustrated in FIG. 4H, the autonomous vehicle is on the left side of object 401, and thus passing object 401 on the right side is infeasible. Likewise, it is infeasible to constrain the autonomous vehicle to track behind object 401 because the autonomous vehicle is already alongside it. A constraint set may also be infeasible based on the incompatibility of a pair of constraints. For example, as illustrated in FIG. 4H, object 403 is to the left of object 402, and thus it would be infeasible to constrain the autonomous vehicle to pass object 402 on the right and object 403 on the left. Distinct combinations of actions may also produce redundant constraint sets. For example, in FIG. 4A, a combination of actions to track behind object 401 and track behind 402 yields an identical constraint set with the combination of actions to track behind object 401 and pass object 402 on the left. In such an example, the autonomous vehicle will never reach object 402 so the action selected has no effect and, therefore, topological planning can cease branching after the action is chosen for object 401. This pruning of redundant constraint sets may be exercised to produce the tree graph 430 in FIG. 4B. Heuristic cost may be used to prioritize constraint sets inputs to trajectory optimization when computation time and resources are limited. For example, in FIG. 4H the constraint sets that pass object 403 on the left might be pruned or assigned lower priority because doing so would cause an undesirable violation of the left lane boundary 406.” (Para 0067), see also Para 0090);
ranking the plurality of passing strategies (“At 350, the system may assign a score to each candidate trajectory, and select (352) a best candidate trajectory based on the assigned scores (e.g., best trajectory selected as maximum reward or minimum cost depending on scoring criteria) to be used for traversing the local region from the optimized trajectories. In certain embodiments, the system may assign a score based on factors such as, without limitation, risk of collision (i.e., a trajectory that has a lesser risk of collision may be assigned a lower cost than a trajectory that has a higher risk of collision), traffic rule violations (i.e. a trajectory that clears an intersection may be assigned lower cost than a trajectory that stops in the intersection and “blocks the box”), passenger comfort (e.g., a trajectory that does not require performing sudden braking or steering maneuvers may be assigned a lower cost than a trajectory that requires such maneuvers), or the like.” (Para 0070), “Therefore, the system may generate optimized trajectories for each of the above constraint sets and select the best trajectory by scoring them based on the current environment of the autonomous vehicle. In some embodiments, the system may also discard the third and the fifth constraint sets above if, for example, lateral margins indicate that passing the static obstacle on the right requires lane mark violation, drivable area violation, or the like. These candidate trajectories would be low priority for optimization and scoring in anytime planning.” (Para 0096), see also Para 0090-0095); and
determining a trajectory along the path for a highest ranked passing strategy (“At 350, the system may assign a score to each candidate trajectory, and select (352) a best candidate trajectory based on the assigned scores (e.g., best trajectory selected as maximum reward or minimum cost depending on scoring criteria) to be used for traversing the local region from the optimized trajectories. In certain embodiments, the system may assign a score based on factors such as, without limitation, risk of collision (i.e., a trajectory that has a lesser risk of collision may be assigned a lower cost than a trajectory that has a higher risk of collision), traffic rule violations (i.e. a trajectory that clears an intersection may be assigned lower cost than a trajectory that stops in the intersection and “blocks the box”), passenger comfort (e.g., a trajectory that does not require performing sudden braking or steering maneuvers may be assigned a lower cost than a trajectory that requires such maneuvers), or the like.” (Para 0070) “Therefore, the system may generate optimized trajectories for each of the above constraint sets and select the best trajectory by scoring them based on the current environment of the autonomous vehicle. In some embodiments, the system may also discard the third and the fifth constraint sets above if, for example, lateral margins indicate that passing the static obstacle on the right requires lane mark violation, drivable area violation, or the like. These candidate trajectories would be low priority for optimization and scoring in anytime planning.” (Para 0096).
In regards to claim 2, Seegmiller discloses of the computer-implemented method of Claim 1, wherein the passing action includes a constraint to pass ahead of or pass behind an object of the one or more objects (“The system may then compute a set of constraints 346 (including one or more constraints) for each topologically distinct class of trajectories determined through topological planning 344, where the set of constraints defines a convex envelope (bounded area) in curvilinear space within which the autonomous vehicle trajectory is confined. As discussed above, the autonomous vehicle can take discrete actions with respect to each object (e.g., to be ahead of or behind something, pass to the left or right, or the like). Each discrete action yields one or more constraints in curvilinear space. For example, the system may consider the following actions which yield the following constraints:” (Para 0052), “FIGS. 5A-5D illustrate the use of topological planning to perform a lane change maneuver in a dense traffic situation 500. The traffic situation 500 includes 4 moving objects (e.g., moving vehicles) in two lanes—501, 502, and 503 in lane 520, and 504 in lane 530. At time t0 (FIG. 5A), the autonomous vehicle 510 is in lane 530 traveling behind object 504, and needs to execute a lane change into lane 520. At time t0 (FIG. 5A), the autonomous vehicle cannot immediately execute a lane change due to being obstructed by objects 501 and 502. However, if the traffic is faster in lane 530 (i.e., 504 is moving faster than 501, 502, and 503), the autonomous vehicle may plan to accelerate and execute a lane change into the gap between objects 502 and 503. At time t1 (FIG. 5B), the autonomous vehicle is ahead of object 502 and behind object 503 in the destination lane 520, and may initiate the lane change maneuver. The autonomous vehicle also decelerates to track behind object 503. FIG. 5C illustrates completion of the lane change maneuver at time t2, using trajectory 541.” (Para 0075), and “Alternatively, the autonomous vehicle may plan to accelerate and execute a lane change into lane 520 after tracking ahead of object 503. FIG. 5D illustrates such a maneuver including completion of the lane change maneuver at time t2, using trajectory 542.” (Para 0076)).
In regards to claim 3, Seegmiller discloses of the computer-implemented method of Claim 1, wherein exhaustive combinations of passing actions form the passing strategies (“The system may perform topologically planning 344 to determine one or more topologically distinct classes in the local region (based on an analysis of the received real-time information). Each topologically distinct class is associated with a set of trajectories that take the same discrete actions with respect to objects in the local region. For example, as shown in FIG. 4A, for traveling a local region 400 between a start position 410 (i.e., the current position of the autonomous vehicle) and a goal position 420 (when the autonomous vehicle will exit the local region), an autonomous vehicle will need to avoid the objects 401, 402, 403. The system may generate the topologically distinct classes by analyzing the spatial relationships the autonomous vehicle can have with the objects 401, 402, 403 with respect to a reference path 405. For the example shown in FIG. 4A, the autonomous vehicle may avoid each of the objects 401, 402, 403 by tracking behind (a longitudinal action), or by passing the object either on the left side or on the right side (a lateral action). The various options are shown as a tree graph 430 in FIG. 4B. Each path 408a-o through the tree graph 430 defines a topologically distinct class of trajectories.” (Para 0050), “A constraint set may be infeasible based on the incompatibility of the state of the autonomous vehicle and a constraint. For example, as illustrated in FIG. 4H, the autonomous vehicle is on the left side of object 401, and thus passing object 401 on the right side is infeasible. Likewise, it is infeasible to constrain the autonomous vehicle to track behind object 401 because the autonomous vehicle is already alongside it. A constraint set may also be infeasible based on the incompatibility of a pair of constraints. For example, as illustrated in FIG. 4H, object 403 is to the left of object 402, and thus it would be infeasible to constrain the autonomous vehicle to pass object 402 on the right and object 403 on the left. Distinct combinations of actions may also produce redundant constraint sets. For example, in FIG. 4A, a combination of actions to track behind object 401 and track behind 402 yields an identical constraint set with the combination of actions to track behind object 401 and pass object 402 on the left. In such an example, the autonomous vehicle will never reach object 402 so the action selected has no effect and, therefore, topological planning can cease branching after the action is chosen for object 401. This pruning of redundant constraint sets may be exercised to produce the tree graph 430 in FIG. 4B. Heuristic cost may be used to prioritize constraint sets inputs to trajectory optimization when computation time and resources are limited. For example, in FIG. 4H the constraint sets that pass object 403 on the left might be pruned or assigned lower priority because doing so would cause an undesirable violation of the left lane boundary 406.” (Para 0067), see also Para 0090).
In regards to claim 4, Seegmiller discloses of the computer-implemented method of Claim 1, wherein the determining the plurality of passing strategies comprises constructing, for each passing strategy, a polygon in space-time, wherein the polygon comprises one or more boundary lines formed from intersection points of the autonomous vehicle with the or each object, wherein the polygon is a travel envelope for the autonomous vehicle (“The system may then compute a set of constraints 346 (including one or more constraints) for each topologically distinct class of trajectories determined through topological planning 344, where the set of constraints defines a convex envelope (bounded area) in curvilinear space within which the autonomous vehicle trajectory is confined. As discussed above, the autonomous vehicle can take discrete actions with respect to each object (e.g., to be ahead of or behind something, pass to the left or right, or the like). Each discrete action yields one or more constraints in curvilinear space. For example, the system may consider the following actions which yield the following constraints:” (Para 0052), “Referring back to FIGS. 4C-4G, different convex envelopes 441, 442, 443, 444, and 445 corresponding to the constraint set for each of the corresponding topologically distinct classes are shown. All possible trajectories for a class are bounded by the corresponding complex envelope generated based on the constraint set for that class. For example, in FIG. 4F, the envelope 444 illustrates the constraints to (1) track behind object 402, and to (2) pass object 401 on the right. The constraints are expressed in curvilinear space with respect to the reference path 405. To track behind object 402 the longitudinal distance along the reference path must not violate an upper bound 454. To pass object 401 on the right, the lateral offset must not violate a left bound over some interval on the reference path.” (Para 0065), see also Para 0083-0085 and Figs 4C-4G and 5E-5G).
In regards to claim 5, Seegmiller discloses of the computer-implemented method of Claim 4, further comprising determining one or more unreachable regions of the polygon, wherein an unreachable region is a region of the polygon that the autonomous vehicle is unable to reach (“Referring back to FIGS. 4C-4G, different convex envelopes 441, 442, 443, 444, and 445 corresponding to the constraint set for each of the corresponding topologically distinct classes are shown. All possible trajectories for a class are bounded by the corresponding complex envelope generated based on the constraint set for that class. For example, in FIG. 4F, the envelope 444 illustrates the constraints to (1) track behind object 402, and to (2) pass object 401 on the right. The constraints are expressed in curvilinear space with respect to the reference path 405. To track behind object 402 the longitudinal distance along the reference path must not violate an upper bound 454. To pass object 401 on the right, the lateral offset must not violate a left bound over some interval on the reference path.” (Para 0065), “Whenever an autonomous vehicle is in a lane and/or is transitioning to/from that lane, it is bound by one of the following longitudinal constraints for each object in the lane: track ahead of the object (if behind the autonomous vehicle), track behind the object (if ahead of the autonomous vehicle), or take an action to pass the object on the left or the right. Using these constraints, the system searches for a transition time interval to execute a lane change such that: Constraints are feasible, i.e. there is no track ahead constraint that would require violation of a track behind constraint. Constraints are achievable, given the autonomous vehicle's current state and dynamic limits. autonomous vehicle must track behind or pass all objects currently ahead of it in origin corridor, autonomous vehicle must respect posted speed limits, and autonomous vehicle must respect comfort limits on longitudinal acceleration.” (Para 0077-0082), and “FIG. 5E illustrates an evaluation of a transition time interval 551 by forming a convex envelope 550 that may be used for generating the lane change trajectory 541 shown in FIGS. 5A-5C. In FIG. 5E, 552, 553, and 554 illustrate the longitudinal constraints for objects 501, 502, and 503 in lane 520 (destination lane), and 555 illustrates the longitudinal constraint for object 504 in lane 530 (origin lane), expressed in curvilinear coordinates with respect to reference paths in the lanes 520 and 530. The distances along are with respect to a shared datum (in this case the current autonomous vehicle location). As shown in FIG. 5E, the convex envelope 550 includes trajectories in which the autonomous vehicle tracks behind object 504 from time t0 to t1, tracks behind objects 504 and 503 and ahead of object 502 while transitioning between lanes from time t1 to t2, and finally tracks behind object 503 and ahead of object 502 after t2. After t2 the autonomous vehicle is entirely in lane 520 and has no constraint with respect to 504.” (Para 0083), see also Figs 4C-4G and 5E-5G).
In regards to claim 6, Seegmiller discloses of the computer-implemented method of Claim 5, wherein the or each unreachable region is determined based on kinodynamic constraints of the autonomous vehicle (“Referring back to FIGS. 4C-4G, different convex envelopes 441, 442, 443, 444, and 445 corresponding to the constraint set for each of the corresponding topologically distinct classes are shown. All possible trajectories for a class are bounded by the corresponding complex envelope generated based on the constraint set for that class. For example, in FIG. 4F, the envelope 444 illustrates the constraints to (1) track behind object 402, and to (2) pass object 401 on the right. The constraints are expressed in curvilinear space with respect to the reference path 405. To track behind object 402 the longitudinal distance along the reference path must not violate an upper bound 454. To pass object 401 on the right, the lateral offset must not violate a left bound over some interval on the reference path.” (Para 0065), “Whenever an autonomous vehicle is in a lane and/or is transitioning to/from that lane, it is bound by one of the following longitudinal constraints for each object in the lane: track ahead of the object (if behind the autonomous vehicle), track behind the object (if ahead of the autonomous vehicle), or take an action to pass the object on the left or the right. Using these constraints, the system searches for a transition time interval to execute a lane change such that: Constraints are feasible, i.e. there is no track ahead constraint that would require violation of a track behind constraint. Constraints are achievable, given the autonomous vehicle's current state and dynamic limits. autonomous vehicle must track behind or pass all objects currently ahead of it in origin corridor, autonomous vehicle must respect posted speed limits, and autonomous vehicle must respect comfort limits on longitudinal acceleration.” (Para 0077-0082), and “FIG. 5E illustrates an evaluation of a transition time interval 551 by forming a convex envelope 550 that may be used for generating the lane change trajectory 541 shown in FIGS. 5A-5C. In FIG. 5E, 552, 553, and 554 illustrate the longitudinal constraints for objects 501, 502, and 503 in lane 520 (destination lane), and 555 illustrates the longitudinal constraint for object 504 in lane 530 (origin lane), expressed in curvilinear coordinates with respect to reference paths in the lanes 520 and 530. The distances along are with respect to a shared datum (in this case the current autonomous vehicle location). As shown in FIG. 5E, the convex envelope 550 includes trajectories in which the autonomous vehicle tracks behind object 504 from time t0 to t1, tracks behind objects 504 and 503 and ahead of object 502 while transitioning between lanes from time t1 to t2, and finally tracks behind object 503 and ahead of object 502 after t2. After t2 the autonomous vehicle is entirely in lane 520 and has no constraint with respect to 504.” (Para 0083), see also Figs 4C-4G and 5E-5G).
In regards to claim 7, Seegmiller discloses of the computer-implemented method of Claim 6, wherein the kinodynamic constraints include one or more from a list including: maximum speed, maximum acceleration, and maximum deceleration (“Referring back to FIGS. 4C-4G, different convex envelopes 441, 442, 443, 444, and 445 corresponding to the constraint set for each of the corresponding topologically distinct classes are shown. All possible trajectories for a class are bounded by the corresponding complex envelope generated based on the constraint set for that class. For example, in FIG. 4F, the envelope 444 illustrates the constraints to (1) track behind object 402, and to (2) pass object 401 on the right. The constraints are expressed in curvilinear space with respect to the reference path 405. To track behind object 402 the longitudinal distance along the reference path must not violate an upper bound 454. To pass object 401 on the right, the lateral offset must not violate a left bound over some interval on the reference path.” (Para 0065), “Whenever an autonomous vehicle is in a lane and/or is transitioning to/from that lane, it is bound by one of the following longitudinal constraints for each object in the lane: track ahead of the object (if behind the autonomous vehicle), track behind the object (if ahead of the autonomous vehicle), or take an action to pass the object on the left or the right. Using these constraints, the system searches for a transition time interval to execute a lane change such that: Constraints are feasible, i.e. there is no track ahead constraint that would require violation of a track behind constraint. Constraints are achievable, given the autonomous vehicle's current state and dynamic limits. autonomous vehicle must track behind or pass all objects currently ahead of it in origin corridor, autonomous vehicle must respect posted speed limits, and autonomous vehicle must respect comfort limits on longitudinal acceleration.” (Para 0077-0082), and “FIG. 5E illustrates an evaluation of a transition time interval 551 by forming a convex envelope 550 that may be used for generating the lane change trajectory 541 shown in FIGS. 5A-5C. In FIG. 5E, 552, 553, and 554 illustrate the longitudinal constraints for objects 501, 502, and 503 in lane 520 (destination lane), and 555 illustrates the longitudinal constraint for object 504 in lane 530 (origin lane), expressed in curvilinear coordinates with respect to reference paths in the lanes 520 and 530. The distances along are with respect to a shared datum (in this case the current autonomous vehicle location). As shown in FIG. 5E, the convex envelope 550 includes trajectories in which the autonomous vehicle tracks behind object 504 from time t0 to t1, tracks behind objects 504 and 503 and ahead of object 502 while transitioning between lanes from time t1 to t2, and finally tracks behind object 503 and ahead of object 502 after t2. After t2 the autonomous vehicle is entirely in lane 520 and has no constraint with respect to 504.” (Para 0083), see also Figs 4C-4G and 5E-5G).
In regards to claim 8, Seegmiller discloses of the computer-implemented method of Claim 5, further comprising shrinking the polygon to exclude the or each unreachable region (“Referring back to FIGS. 4C-4G, different convex envelopes 441, 442, 443, 444, and 445 corresponding to the constraint set for each of the corresponding topologically distinct classes are shown. All possible trajectories for a class are bounded by the corresponding complex envelope generated based on the constraint set for that class. For example, in FIG. 4F, the envelope 444 illustrates the constraints to (1) track behind object 402, and to (2) pass object 401 on the right. The constraints are expressed in curvilinear space with respect to the reference path 405. To track behind object 402 the longitudinal distance along the reference path must not violate an upper bound 454. To pass object 401 on the right, the lateral offset must not violate a left bound over some interval on the reference path.” (Para 0065), “A constraint set may be infeasible based on the incompatibility of the state of the autonomous vehicle and a constraint. For example, as illustrated in FIG. 4H, the autonomous vehicle is on the left side of object 401, and thus passing object 401 on the right side is infeasible. Likewise, it is infeasible to constrain the autonomous vehicle to track behind object 401 because the autonomous vehicle is already alongside it. A constraint set may also be infeasible based on the incompatibility of a pair of constraints. For example, as illustrated in FIG. 4H, object 403 is to the left of object 402, and thus it would be infeasible to constrain the autonomous vehicle to pass object 402 on the right and object 403 on the left. Distinct combinations of actions may also produce redundant constraint sets. For example, in FIG. 4A, a combination of actions to track behind object 401 and track behind 402 yields an identical constraint set with the combination of actions to track behind object 401 and pass object 402 on the left. In such an example, the autonomous vehicle will never reach object 402 so the action selected has no effect and, therefore, topological planning can cease branching after the action is chosen for object 401. This pruning of redundant constraint sets may be exercised to produce the tree graph 430 in FIG. 4B. Heuristic cost may be used to prioritize constraint sets inputs to trajectory optimization when computation time and resources are limited. For example, in FIG. 4H the constraint sets that pass object 403 on the left might be pruned or assigned lower priority because doing so would cause an undesirable violation of the left lane boundary 406.” (Para 0067), “The system may then compute a set of constraints 346 (including one or more constraints) for each topologically distinct class of trajectories determined through topological planning 344, where the set of constraints defines a convex envelope (bounded area) in curvilinear space within which the autonomous vehicle trajectory is confined. As discussed above, the autonomous vehicle can take discrete actions with respect to each object (e.g., to be ahead of or behind something, pass to the left or right, or the like). Each discrete action yields one or more constraints in curvilinear space. For example, the system may consider the following actions which yield the following constraints:” (Para 0052), see also Figs 4C-4G and 5E-5G; wherein the envelope of the possible driving routes are considered according to constraints, where the envelope doesn’t consider those outside the constraints and is therefore shrunk).
In regards to claim 9, Seegmiller discloses of the computer-implemented method of Claim 4, wherein ranking the passing strategies comprises comparing geometric features of each polygon and ranking the passing strategies based on the comparisons (“At 350, the system may assign a score to each candidate trajectory, and select (352) a best candidate trajectory based on the assigned scores (e.g., best trajectory selected as maximum reward or minimum cost depending on scoring criteria) to be used for traversing the local region from the optimized trajectories. In certain embodiments, the system may assign a score based on factors such as, without limitation, risk of collision (i.e., a trajectory that has a lesser risk of collision may be assigned a lower cost than a trajectory that has a higher risk of collision), traffic rule violations (i.e. a trajectory that clears an intersection may be assigned lower cost than a trajectory that stops in the intersection and “blocks the box”), passenger comfort (e.g., a trajectory that does not require performing sudden braking or steering maneuvers may be assigned a lower cost than a trajectory that requires such maneuvers), or the like.” (Para 0070), “Therefore, the system may generate optimized trajectories for each of the above constraint sets and select the best trajectory by scoring them based on the current environment of the autonomous vehicle. In some embodiments, the system may also discard the third and the fifth constraint sets above if, for example, lateral margins indicate that passing the static obstacle on the right requires lane mark violation, drivable area violation, or the like. These candidate trajectories would be low priority for optimization and scoring in anytime planning.” (Para 0096), see also Para 0090-0095).
In regards to claim 10, Seegmiller discloses of the computer-implemented method of Claim 9, wherein the geometric features include a polygon area and a width of a pinch point. (“The system may then compute a set of constraints 346 (including one or more constraints) for each topologically distinct class of trajectories determined through topological planning 344, where the set of constraints defines a convex envelope (bounded area) in curvilinear space within which the autonomous vehicle trajectory is confined. As discussed above, the autonomous vehicle can take discrete actions with respect to each object (e.g., to be ahead of or behind something, pass to the left or right, or the like). Each discrete action yields one or more constraints in curvilinear space. For example, the system may consider the following actions which yield the following constraints: With respect to restricted areas, such as intersections and crosswalks: STOP—yields a constraint to stop before entering a restricted area; and PROCEED—no constraint.” (Para 0052-0055), “Therefore, the system may generate optimized trajectories for each of the above constraint sets and select the best trajectory by scoring them based on the current environment of the autonomous vehicle. In some embodiments, the system may also discard the third and the fifth constraint sets above if, for example, lateral margins indicate that passing the static obstacle on the right requires lane mark violation, drivable area violation, or the like. These candidate trajectories would be low priority for optimization and scoring in anytime planning.” (Para 0096), “Referring back to FIGS. 4C-4G, different convex envelopes 441, 442, 443, 444, and 445 corresponding to the constraint set for each of the corresponding topologically distinct classes are shown. All possible trajectories for a class are bounded by the corresponding complex envelope generated based on the constraint set for that class. For example, in FIG. 4F, the envelope 444 illustrates the constraints to (1) track behind object 402, and to (2) pass object 401 on the right. The constraints are expressed in curvilinear space with respect to the reference path 405. To track behind object 402 the longitudinal distance along the reference path must not violate an upper bound 454. To pass object 401 on the right, the lateral offset must not violate a left bound over some interval on the reference path.” (Para 0065), “FIG. 6C illustrates lateral constraints for each of the objects 601, 602, and 603 as lateral distance with respect to distance along the reference path 611. The lines 607, 608 and 609 illustrate candidate trajectories taking into account only the lateral constraints: stop before the static object 601 (607); pass the static object 601 on the left (608) or on the right (609). The autonomous vehicle need not veer to pass the object 602. However, the lateral distance to the object 602 is a function of time (and hence must be considered based on the corresponding longitudinal constraints)—621 is before the object 602 crosses the lane 612 (at time t1) and 622 is after the object 602 crosses the lane 612 (at time t2).” (Para 0098)).
In regards to claim 11, Seegmiller discloses of the computer-implemented method of Claim 4, further comprising disregarding a passing strategy as infeasible when the respective polygon does not include the autonomous vehicle at the original of the polygon (“To generate the nominal route, the system may first identify candidate routes that a vehicle can travel on to get from the start position to the destination. The system may then score the candidate routes and identify a nominal route to reach the destination. For example, the system may generate a nominal route that minimizes Euclidean distance traveled or other cost function during the route. Depending on implementation, the system may generate one or more routes using various routing methods, such as Dijkstra's algorithm, Bellman-Ford algorithm, or other algorithms. The routing module 112(b) may also use the traffic information to generate a navigation route that reflects expected conditions of the route (e.g., current day of the week or current time of day, etc.), such that a route generated for travel during rush-hour may differ from a route generated for travel late at night.” (Para 0044), see also Para 0047).
In regards to claim 12, Seegmiller discloses of the computer-implemented method of Claim 1, wherein the positional information includes a predicted trajectory of the object (“The prediction subsystem 123 may predict the future locations, trajectories, and/or actions of the objects based at least in part on perception information (e.g., the state data for each object) received from the perception subsystem 122, the location information received from the location subsystem 121, the sensor data, and/or any other data that describes the past and/or current state of the objects, the autonomous vehicle 101, the surrounding environment, and/or their relationship(s). For example, if an object is a vehicle and the current driving environment includes an intersection, prediction subsystem 123 may predict whether the object will likely move straight forward or make a turn. If the perception data indicates that the intersection has no traffic light, prediction subsystem 123 may also predict whether the vehicle may have to fully stop prior to enter the intersection. Such predictions may be made for a given time horizon (e.g., 5 seconds in the future). In certain embodiments, the prediction subsystem 123 may provide the predicted trajector(ies) for each object to the motion planning subsystem 124.” (Para 0033)).
In regards to claim 13, the claim recites analogous limitations to claim 1, and is therefore rejected on the same premise.
In regards to claim 14, the claim recites analogous subject matter to claim 1 and is rejected on the same premise, but further teaches one or more actuators configured to maneuver the autonomous vehicle along the trajectory (“As discussed above, planning and control data regarding the movement of the autonomous vehicle is generated by the motion planning subsystem 124 of the controller 120 that is transmitted to the vehicle control system 113 for execution. The vehicle control system 113 may, for example, control braking via a brake controller; direction via a steering controller; speed and acceleration via a throttle controller (in a gas-powered vehicle) or a motor speed controller (such as a current level controller in an electric vehicle); a differential gear controller (in vehicles with transmissions); and/or other controllers.” (Para 0039), see also Para 0025).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Shah (US 20220185266) discloses of adjusting a size of a polygon based on the relevance of the size of it, where it can decrease according to speed of the vehicle.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Kyle J Kingsland whose telephone number is (571)272-3268. The examiner can normally be reached Mon-Fri 8:00-4:30.
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/KYLE J KINGSLAND/ Primary Examiner, Art Unit 3663