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 .
Response to Arguments
Applicants arguments filed 9/2/2025 have been fully considered as follows:
Applicant argues that the 35 USC 103 rejections to the claims should not be maintained in view of “Applicant respectfully asserts that Djuric and Hiramatsu both fail to disclose generating a reference path having a gentlest curve form from a current location of the object to the end point, and also fail to disclose determining, as the predicted path of the object, a candidate path having a smallest error from the reference path among at least one candidate path derivable based on the dynamics information of the object. Therefore, Applicant respectfully asserts that Djuric and Hiramatsu, individually or in any combination, fail to disclose the above-recited features of amended independent claim 1” This argument is persuasive. Therefore, a new ground of rejection is below.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-5 and 8-20 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
As per Claims 1, 15, and 18, the specification does not describe in a clear, concise, and exact terms how the reference path generates a “gentlest curve” or how the candidate path determines a “smallest error”. The curves described in paragraphs [0016, 0061, 0085-0086, 0117] and errors in paragraphs [0016, 0061, 0081, 0088, 0117] are directed to an end result and do not provide clear and exact terms how to generate a reference path having the gentlest curve or determine a candidate path having the smallest error. Claims 2-5, 8-14, 16-17, and 19-20 depending from claims 1, 15, and 18 are therefore rejected.
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.
Claims 1-5 and 8-20 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.
Claims 1, 15, and 18 recite “gentlest curve” and “smallest error”. There is insufficient antecedent basis for this limitation in the claim. It is unclear how one skilled in the art would generate a reference path having the gentlest curve or determine a candidate path having the smallest error. Claims 2-5, 8-14, 16-17, and 19-20 depending from claims 1, 15, and 18 are therefore rejected.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Djuric (US 20190049970 A1) in view of Hiramatsu (US 20210261162 A1) in further view of Houshmand (US 20220194419 A1)
Regarding claim 1, Djuric teaches An apparatus for controlling a vehicle, the apparatus comprising: ([0002] The present disclosure relates generally to predicting the motion of objects proximate to an autonomous vehicle and controlling the autonomous vehicle)
one or more processors: and ([0006] The computing system includes one or more processors )
a memory storing program instructions which, when executed by the one or more processors, cause the one or more processors to: ([0006] The computing system includes one or more processors and one or more tangible, non-transitory, computer readable media that collectively store instructions that when executed by the one or more processors cause the computing system to perform operations.)
select an object intersecting the vehicle at an intersection existing on a driving path of the vehicle; ([0084] For instance, the model 136 can process the combined data set 137 (e.g., the fused state data) to determine that the object 810 (e.g., a vehicle) is travelling at a certain speed, in the direction of the intersection shown in FIG. 8, etc. The model 136 can also process the combined data set 137 (e.g., the fused map data) to determine that the object 810 is traveling within a particular travel lane as it approaches the intersection, the color of the traffic light, etc. In some implementations, the model 136 can process the nominal pathway information to determine which nominal pathway(s) the object 810 may be likely to take (e.g., based on previously observed actions for that type of vehicle).)
determine a driving method of the vehicle based on a risk determination result. ([0077] For instance, given the predicted trajectories 502, 504 of objects 202, 302, the motion planning system 128 can determine a motion plan 134 for the vehicle 104 that best navigates the vehicle 104 relative to the objects 202, 302 at their future locations.)
determine information about a first area having a relatively low degree of freedom in order to process objects travelling on an unconnected lane within the intersection in a consistent manner; and ([0088] The first way-point confidence level 815A can be represented by a circle centered on the first way-point 820A, where the radius of the circle is indicative of the confidence (e.g., the smaller the circle's radius the higher the confidence and the larger the circle's radius the lower the confidence, the larger the circle's radius the higher the uncertainty and the smaller the circle's radius the lower the uncertainty). The variation in confidence level can arise from the real-world observation that objects can operate with at least some degree of variation (e.g., various driving styles, various biking styles, various walking speeds, etc.) [0093] the first predicted trajectory 805A (which is indicative of a right-hand turn at the intersection).)
calculate the predicted path most suitable for the determined information based on a plurality of prediction paths in a second area with a high degree of freedom, ([0088] By way of example, as shown in FIG. 8, the model 136 can determine and output a first way-point confidence level 815A for a first way-point 820A of the first predicted trajectory 805A. The first way-point confidence level 815A can be represented by a circle centered on the first way-point 820A, where the radius of the circle is indicative of the confidence (e.g., the smaller the circle's radius the higher the confidence and the larger the circle's radius the lower the confidence, the larger the circle's radius the higher the uncertainty and the smaller the circle's radius the lower the uncertainty). The variation in confidence level can arise from the real-world observation that objects can operate with at least some degree of variation (e.g., various driving styles, various biking styles, various walking speeds, etc.). Additionally, or alternatively, the model 136 can determine and output a second way-point confidence level 815B for a second way-point 820B of the second predicted trajectory 805B. The second way-point confidence level 815B can be represented by a circle centered on the second way-point 820B, where the radius of the circle is indicative of the confidence/uncertainty associated with that particular predicted future location of the object.)
wherein a degree of freedom includes a degree at which the objects do not follow a driving line (Fig. 5 object 302 predicted trajectory 504 [0076] The motion planning system 128 can generate a motion plan 134 for the vehicle 104 based at least in part on the output 406 (and/or other data indicative of the predicted trajectory 502, 504 of an object 202, 302).
and calculate an end point at which the object is determined to advance with the highest probability based on a driving path of the object or dynamics information among a plurality of end points from which the object advance from the intersection. ([0088] The first way-point confidence level 815A can be represented by a circle centered on the first way-point 820A, where the radius of the circle is indicative of the confidence (e.g., the smaller the circle's radius the higher the confidence)…the model 136 can determine and output a second way-point confidence level 815B for a second way-point 820B of the second predicted trajectory 805B)
Djuric does not expressly disclose but Hiramatsu discloses determine a risk during driving of the vehicle based on a predicted path of the object; and ([0049] A process advances to step S02, and the controller 2 assumes or detects another vehicle that has a risk of coming into contact with the host-vehicle 20. Specifically, the intersecting point estimating unit 13 predicts the first other vehicle track 31 of the first other vehicle 21 from the position, the traveling direction and the speed of the first other vehicle 21. The intersecting point estimating unit 13 predicts first other vehicle tracks 31 of all other vehicles present around the host-vehicle 20 detected by the surrounding environment detecting unit 12. On the other hand, the intersecting point estimating unit 13 predicts the host-vehicle track 30.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify Djuric with the teachings of Hiramatsu with a reasonable expectation of success by reducing the amount of information to be processed and suppress delays in driving support of a host-vehicle as taught by Hiramatsu ([0004]).
Djuric does not expressly disclose but Houshmand discloses generate a reference path having a gentlest curve form from a current location of the object to the end point; and ([0042] the trajectory generation component 232 to determine a smooth trajectory and/or to complete a smooth path.)
determine, as the predicted path of the object, a candidate path having a smallest error from the reference path among at least one candidate path derivable based on the dynamics information of the object. ([0061] the cost may be based on the distance to a nearest object and/or a speed and/or direction of travel of a nearest dynamic object. The cost may additionally or alternatively be based at least in part on a deviation cost that is based at least in part on a deviation of the position, heading, velocity, and/or curvature specified by the position in the multivariate space from the route or other reference point (e.g., a target lane) and/or a displacement along the route. )
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify Djuric with the teachings of Houshmand with a reasonable expectation of success by allowing the autonomous vehicle to send a request to teleoperations and receive the selected trajectory from teleoperations without needing to bring the autonomous vehicle to a stop as taught by Houshmand ([0013]).
Regarding claim 2, Djuric does not expressly disclose but Hiramatsu discloses The apparatus of claim 1, wherein execution of the program instructions by the one or more processors further cause the one or more processors to: ([0030] The controller 2 can be realized by using a microcomputer including a CPU (a central processing unit) that is an example of a control unit, a memory (a main storage device), and an input/output unit. ) extract at least one candidate path that intersects the driving path of the vehicle, and select an object that simultaneously intersects the vehicle from among one or more objects traveling along the candidate path. (Fig. 4A [0034] An intersecting point at the time of “merging” is a point (a junction) at which the host-vehicle track 30 and the first other vehicle track 31 overlap with each other for the first time as illustrated in FIG. 4A. )
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify Djuric with the teachings of Hiramatsu with a reasonable expectation of success by reducing the amount of information to be processed and suppress delays in driving support of a host-vehicle as taught by Hiramatsu ([0004]).
Regarding claim 3, Djuric does not expressly disclose but Hiramatsu discloses The apparatus of claim 1, wherein execution of the program instructions by the one or more processors further cause the one or more processors to: ([0030] The controller 2 can be realized by using a microcomputer including a CPU (a central processing unit) that is an example of a control unit, a memory (a main storage device), and an input/output unit. ) calculate, using an object selection device, an intersection of the vehicle and the object and to select the object based on an occupancy time at the intersection of the vehicle and the object. ([0034] An intersecting point at the time of “intersecting” is a point at which the host-vehicle track 30 and another vehicle track intersect with each other. An intersecting point at the time of “merging” is a point (a junction) at which the host-vehicle track 30 and the first other vehicle track 31 overlap with each other for the first time as illustrated in FIG. 4A)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify Djuric with the teachings of Hiramatsu with a reasonable expectation of success by reducing the amount of information to be processed and suppress delays in driving support of a host-vehicle as taught by Hiramatsu ([0004]).
Regarding claim 4, Djuric does not expressly disclose but Hiramatsu discloses The apparatus of claim 1, wherein execution of the program instructions by the one or more processors further cause the one or more processors to: ([0030] The controller 2 can be realized by using a microcomputer including a CPU (a central processing unit) that is an example of a control unit, a memory (a main storage device), and an input/output unit. ) calculate an end point at which the object exits the intersection based on the driving path of the object and the dynamics information. ([0032] The intersecting point estimating unit 13 estimates an intersecting point P at which the host-vehicle track 30 and the first other vehicle track 31 intersect with each other… For example, the intersecting point estimating unit 13 predicts that the first other vehicle 21 would travel straight on a currently traveling lane without change at an intersecting point from the position, the traveling direction and the speed of the first other vehicle 21)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify Djuric with the teachings of Hiramatsu with a reasonable expectation of success by reducing the amount of information to be processed and suppress delays in driving support of a host-vehicle as taught by Hiramatsu ([0004]).
Regarding claim 5, Djuric does not expressly disclose but Hiramatsu discloses The apparatus of claim 4, wherein execution of the program instructions by the one or more processors further cause the one or more processors to: ([0030] The controller 2 can be realized by using a microcomputer including a CPU (a central processing unit) that is an example of a control unit, a memory (a main storage device), and an input/output unit. ) calculate the end point through a first learning model based on the driving path of the object and the dynamics information. ([0032] The intersecting point estimating unit 13 estimates an intersecting point P at which the host-vehicle track 30 and the first other vehicle track 31 intersect with each other… For example, the intersecting point estimating unit 13 predicts that the first other vehicle 21 would travel straight on a currently traveling lane without change at an intersecting point from the position, the traveling direction and the speed of the first other vehicle 21)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify Djuric with the teachings of Hiramatsu with a reasonable expectation of success by reducing the amount of information to be processed and suppress delays in driving support of a host-vehicle as taught by Hiramatsu ([0004]).
Regarding claim 8, Djuric teaches The apparatus of claim 6, wherein execution of the program instructions by the one or more processors further cause the one or more processors to: ([0006] The computing system includes one or more processors and one or more tangible, non-transitory, computer readable media that collectively store instructions that when executed by the one or more processors cause the computing system to perform operations.) determine, using a risk determination device, as the predicted path of the object, a path calculated through a second learning model based on at least one path derived based on the driving path of the object, and the dynamics information of the object. ([0006] The operations include generating image data associated with the object based at least in part on a fusion of the state data associated with the object and the data associated with the geographic area in which the object is located. The operations include determining a plurality of predicted trajectories of the object based at least in part on the image data associated with the object and a machine-learned model.[0021] Accordingly, the systems and methods described herein can improve the speed, quality, and/or accuracy of the generated predictions. Moreover, the improved ability to predict future object location(s) can enable improved motion planning or other control of the autonomous vehicle, thereby enhancing vehicle/passenger/object safety and vehicle efficiency.)
Regarding claim 9, Djuric does not expressly disclose but Hiramatsu discloses The apparatus of claim 1, wherein execution of the program instructions by the one or more processors further cause the one or more processors to: ([0030] The controller 2 can be realized by using a microcomputer including a CPU (a central processing unit) that is an example of a control unit, a memory (a main storage device), and an input/output unit. ) determine the risk considering a time for the vehicle to reach an intersection of the vehicle and the object ([0020] A broader concept that the host-vehicle track 30 intersects with the first other vehicle track (31, and 31 a to 31 c) includes a lower concept that the host-vehicle track 30 merges to the first other vehicle track (31) as illustrated in FIG. 4A to FIG. 4D and a lower concept that the host-vehicle track 30 intersects with the first other vehicle track (31 a to 31 c) as illustrated in FIG. 4E.), a time for the vehicle to pass through the intersection of the vehicle and the object, a time for the object to reach the intersection of the vehicle and the object, and a time for the object to pass through the intersection of the vehicle and the object. ([0034] n intersecting point at the time of “intersecting” is a point at which the host-vehicle track 30 and another vehicle track intersect with each other. An intersecting point at the time of “merging” is a point (a junction) at which the host-vehicle track 30 and the first other vehicle track 31 overlap with each other for the first time as illustrated in FIG. 4A. The intersecting point estimating unit 13 specifies intersecting points P of all other vehicle tracks that intersect with the host-vehicle track 30)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify Djuric with the teachings of Hiramatsu with a reasonable expectation of success by reducing the amount of information to be processed and suppress delays in driving support of a host-vehicle as taught by Hiramatsu ([0004]).
Regarding claim 10, Djuric does not expressly disclose but Hiramatsu discloses The apparatus of claim 1, wherein execution of the program instructions by the one or more processors further cause the one or more processors to: ([0030] The controller 2 can be realized by using a microcomputer including a CPU (a central processing unit) that is an example of a control unit, a memory (a main storage device), and an input/output unit. ) determine the driving method in which a minimum distance between the vehicle and the object is equal to or greater than a reference distance. ([0038] This enables setting a distance between the host-vehicle track 30 and the second other vehicle track 32 to have a longer than predetermined value and eliminating a risk of contact.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify Djuric with the teachings of Hiramatsu with a reasonable expectation of success by reducing the amount of information to be processed and suppress delays in driving support of a host-vehicle as taught by Hiramatsu ([0004]).
Regarding claim 11, Djuric does not expressly disclose but Hiramatsu discloses The apparatus of claim 1, wherein execution of the program instructions by the one or more processors further cause the one or more processors to: ([0030] The controller 2 can be realized by using a microcomputer including a CPU (a central processing unit) that is an example of a control unit, a memory (a main storage device), and an input/output unit. ) calculate a control parameter of the vehicle according to the driving method. ([0095] FIG. 5A is a top view illustrating a sixth traveling scene. In a case where a distance between the second other vehicle track 32 and the host-vehicle track 30 a is equal to or less than a predetermined value, there is a risk of contact between the host-vehicle 20 and the second other vehicle 22. In the sixth traveling scene, to eliminate a risk of contact between the second other vehicle 22 and the host-vehicle 20, the host-vehicle track 30 a is corrected such that the distance between the host-vehicle track 30 and the second other vehicle track 32 becomes longer than the predetermined value)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify Djuric with the teachings of Hiramatsu with a reasonable expectation of success by reducing the amount of information to be processed and suppress delays in driving support of a host-vehicle as taught by Hiramatsu ([0004]).
Regarding claim 12, Djuric does not expressly disclose but Hiramatsu discloses The apparatus of claim 11, wherein the control parameter includes the driving path and a speed profile of the vehicle. ([0095] FIG. 5A is a top view illustrating a sixth traveling scene. In a case where a distance between the second other vehicle track 32 and the host-vehicle track 30 a is equal to or less than a predetermined value, there is a risk of contact between the host-vehicle 20 and the second other vehicle 22. In the sixth traveling scene, to eliminate a risk of contact between the second other vehicle 22 and the host-vehicle 20, the host-vehicle track 30 a is corrected such that the distance between the host-vehicle track 30 and the second other vehicle track 32 becomes longer than the predetermined value)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify Djuric with the teachings of Hiramatsu with a reasonable expectation of success by reducing the amount of information to be processed and suppress delays in driving support of a host-vehicle as taught by Hiramatsu ([0004]).
Regarding claim 13, Djuric does not expressly disclose but Hiramatsu discloses The apparatus of claim 1, wherein execution of the program instructions by the one or more processors further cause the one or more processors to: ([0030] The controller 2 can be realized by using a microcomputer including a CPU (a central processing unit) that is an example of a control unit, a memory (a main storage device), and an input/output unit. ) determine the driving method by scheduling the driving path of the vehicle and the predicted path of the object by time. ([0095] FIG. 5A is a top view illustrating a sixth traveling scene. In a case where a distance between the second other vehicle track 32 and the host-vehicle track 30 a is equal to or less than a predetermined value, there is a risk of contact between the host-vehicle 20 and the second other vehicle 22. In the sixth traveling scene, to eliminate a risk of contact between the second other vehicle 22 and the host-vehicle 20, the host-vehicle track 30 a is corrected such that the distance between the host-vehicle track 30 and the second other vehicle track 32 becomes longer than the predetermined value)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify Djuric with the teachings of Hiramatsu with a reasonable expectation of success by reducing the amount of information to be processed and suppress delays in driving support of a host-vehicle as taught by Hiramatsu ([0004]).
Regarding claim 14, Djuric does not expressly disclose but Hiramatsu discloses The apparatus of claim 1, wherein execution of the program instructions by the one or more processors further cause the one or more processors to: ([0030] The controller 2 can be realized by using a microcomputer including a CPU (a central processing unit) that is an example of a control unit, a memory (a main storage device), and an input/output unit. ) select the object from a nearest intersection in the driving path of the vehicle among at least one intersection existing on the driving path of the vehicle. ([0100] FIG. 5B is a top view illustrating a seventh traveling scene. In the seventh traveling scene also, as similar to the sixth traveling scene, to eliminate a risk of contact between the second other vehicle 22 and the host-vehicle 20, the host-vehicle track 30 a is corrected such that the distance between the host-vehicle track 30 and the second other vehicle track 32 becomes longer than the predetermined value.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify Djuric with the teachings of Hiramatsu with a reasonable expectation of success by reducing the amount of information to be processed and suppress delays in driving support of a host-vehicle as taught by Hiramatsu ([0004]).
Regarding claim 15, Djuric teaches A vehicle system comprising: ([0002] The present disclosure relates generally to predicting the motion of objects proximate to an autonomous vehicle and controlling the autonomous vehicle)
one or more processors; and ([0006] The computing system includes one or more processors )
a memory storing program instructions which, when executed by the one or more processors, cause the one or more processors to: ([0006] The computing system includes one or more processors and one or more tangible, non-transitory, computer readable media that collectively store instructions that when executed by the one or more processors cause the computing system to perform operations.)
select an object intersecting the vehicle at an intersection existing on a driving path of the vehicle ([0084] For instance, the model 136 can process the combined data set 137 (e.g., the fused state data) to determine that the object 810 (e.g., a vehicle) is travelling at a certain speed, in the direction of the intersection shown in FIG. 8, etc. The model 136 can also process the combined data set 137 (e.g., the fused map data) to determine that the object 810 is traveling within a particular travel lane as it approaches the intersection, the color of the traffic light, etc. In some implementations, the model 136 can process the nominal pathway information to determine which nominal pathway(s) the object 810 may be likely to take (e.g., based on previously observed actions for that type of vehicle).), determine a driving method of the vehicle based on a risk of the vehicle determined based on a predicted path of the object ([0077] For instance, given the predicted trajectories 502, 504 of objects 202, 302, the motion planning system 128 can determine a motion plan 134 for the vehicle 104 that best navigates the vehicle 104 relative to the objects 202, 302 at their future locations.),
determine information about a first area having a relatively low degree of freedom in order to process objects travelling on an unconnected lane within the intersection in a consistent manner; and ([0088] The first way-point confidence level 815A can be represented by a circle centered on the first way-point 820A, where the radius of the circle is indicative of the confidence (e.g., the smaller the circle's radius the higher the confidence and the larger the circle's radius the lower the confidence, the larger the circle's radius the higher the uncertainty and the smaller the circle's radius the lower the uncertainty). The variation in confidence level can arise from the real-world observation that objects can operate with at least some degree of variation (e.g., various driving styles, various biking styles, various walking speeds, etc.) [0093] the first predicted trajectory 805A (which is indicative of a right-hand turn at the intersection).)
calculate the predicted path most suitable for the determined information based on a plurality of prediction oaths in a second area with a high degree of freedom, ([0088] By way of example, as shown in FIG. 8, the model 136 can determine and output a first way-point confidence level 815A for a first way-point 820A of the first predicted trajectory 805A. The first way-point confidence level 815A can be represented by a circle centered on the first way-point 820A, where the radius of the circle is indicative of the confidence (e.g., the smaller the circle's radius the higher the confidence and the larger the circle's radius the lower the confidence, the larger the circle's radius the higher the uncertainty and the smaller the circle's radius the lower the uncertainty). The variation in confidence level can arise from the real-world observation that objects can operate with at least some degree of variation (e.g., various driving styles, various biking styles, various walking speeds, etc.). Additionally, or alternatively, the model 136 can determine and output a second way-point confidence level 815B for a second way-point 820B of the second predicted trajectory 805B. The second way-point confidence level 815B can be represented by a circle centered on the second way-point 820B, where the radius of the circle is indicative of the confidence/uncertainty associated with that particular predicted future location of the object.)
wherein a degree of freedom includes a degree at which the objects do not follow a driving line: (Fig. 5 object 302 predicted trajectory 504 [0076] The motion planning system 128 can generate a motion plan 134 for the vehicle 104 based at least in part on the output 406 (and/or other data indicative of the predicted trajectory 502, 504 of an object 202, 302)
calculate an end point at which the object is determined to advance with the highest probability based on a driving oath of the object or dynamics information among a plurality of end points from which the object advance from the intersection; ([0088] The first way-point confidence level 815A can be represented by a circle centered on the first way-point 820A, where the radius of the circle is indicative of the confidence (e.g., the smaller the circle's radius the higher the confidence)…the model 136 can determine and output a second way-point confidence level 815B for a second way-point 820B of the second predicted trajectory 805B)
and control the vehicle based on a control parameter according to the driving method of the vehicle. ([0103] At (614), the method 600 can include controlling a motion of the vehicle based at least in part on the output. For instance, the vehicle computing system 102 can control a motion of the vehicle 104 based at least in part on the output 406 from the model 136 (e.g., the machine-learned model). The vehicle computing system 102 can generate a motion plan 134 for the vehicle 104 based at least in part on the output 406, as described herein. The vehicle computing system 102 can cause the vehicle 104 to travel in accordance with the motion plan 134)
Djuric does not expressly disclose but Hiramatsu discloses a sensor configured to detect an object around a vehicle; ([0023] The driving support device includes a sensor unit 1 that acquires a road environment around the host-vehicle 20 and a surrounding environment of the host-vehicle 20, a controller 2 that determines whether the host-vehicle 20 can enter the intersecting point P based on the acquired road environment and surrounding environment, and a travel control unit 3 that controls travelling of the host-vehicle 20 based on a determination result of the controller 2.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify Djuric with the teachings of Hiramatsu with a reasonable expectation of success by reducing the amount of information to be processed and suppress delays in driving support of a host-vehicle as taught by Hiramatsu ([0004]).
Djuric does not expressly disclose but Houshmand discloses generate a reference path having a gentlest curve form from a current location of the object to the end point; and ([0042] the trajectory generation component 232 to determine a smooth trajectory and/or to complete a smooth path.)
determine, as the predicted path of the object, a candidate path having a smallest error from the reference path among at least one candidate path derivable based on the dynamics information of the object. ([0061] the cost may be based on the distance to a nearest object and/or a speed and/or direction of travel of a nearest dynamic object. The cost may additionally or alternatively be based at least in part on a deviation cost that is based at least in part on a deviation of the position, heading, velocity, and/or curvature specified by the position in the multivariate space from the route or other reference point (e.g., a target lane) and/or a displacement along the route. )
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify Djuric with the teachings of Houshmand with a reasonable expectation of success by allowing the autonomous vehicle to send a request to teleoperations and receive the selected trajectory from teleoperations without needing to bring the autonomous vehicle to a stop as taught by Houshmand ([0013]).
Regarding claim 16, Djuric does not expressly disclose but Hiramatsu discloses The vehicle system of claim 15, wherein the sensor is configured to detect information about a driving state of the vehicle. ([0023] The driving support device includes a sensor unit 1 that acquires a road environment around the host-vehicle 20 and a surrounding environment of the host-vehicle 20, a controller 2 that determines whether the host-vehicle 20 can enter the intersecting point P based on the acquired road environment and surrounding environment, and a travel control unit 3 that controls travelling of the host-vehicle 20 based on a determination result of the controller 2.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify Djuric with the teachings of Hiramatsu with a reasonable expectation of success by reducing the amount of information to be processed and suppress delays in driving support of a host-vehicle as taught by Hiramatsu ([0004]).
Regarding claim 17, Djuric does not expressly disclose but Hiramatsu discloses The vehicle system of claim 15, wherein execution of the program instructions by the one or more processors further cause the one or more processors to: ([0030] The controller 2 can be realized by using a microcomputer including a CPU (a central processing unit) that is an example of a control unit, a memory (a main storage device), and an input/output unit. ) obtain location information of the vehicle and map information from an external server. ([0026] The data indicating the road structure may be stored in the external storage device 4 as a part of the road map data. Further, a current position of the host-vehicle 20 (a self-position) may be obtained by using a GPS receiver that is mounted on the host-vehicle 20 and receives a radio wave from a GPS satellite. Alternatively, the self-position may be detected by using one of GPS, odometry, dead reckoning, and map matching using surrounding images singly or using some of them in combination.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify Djuric with the teachings of Hiramatsu with a reasonable expectation of success by reducing the amount of information to be processed and suppress delays in driving support of a host-vehicle as taught by Hiramatsu ([0004]).
Regarding claim 18, Djuric teaches A method of controlling a vehicle, the method comprising: ([0002] The present disclosure relates generally to predicting the motion of objects proximate to an autonomous vehicle and controlling the autonomous vehicle)
selecting, by a processor ([0006] The computing system includes one or more processors ), an object intersecting the vehicle at an intersection existing on a driving path of the vehicle; ([0084] For instance, the model 136 can process the combined data set 137 (e.g., the fused state data) to determine that the object 810 (e.g., a vehicle) is travelling at a certain speed, in the direction of the intersection shown in FIG. 8, etc. The model 136 can also process the combined data set 137 (e.g., the fused map data) to determine that the object 810 is traveling within a particular travel lane as it approaches the intersection, the color of the traffic light, etc. In some implementations, the model 136 can process the nominal pathway information to determine which nominal pathway(s) the object 810 may be likely to take (e.g., based on previously observed actions for that type of vehicle).)
determining, by the processor ([0006] The computing system includes one or more processors ), a driving method of the vehicle based on a risk determination result. ([0077] For instance, given the predicted trajectories 502, 504 of objects 202, 302, the motion planning system 128 can determine a motion plan 134 for the vehicle 104 that best navigates the vehicle 104 relative to the objects 202, 302 at their future locations.)
determining, by the processor, information about a first area having a relatively low degree of freedom in order to process objects travelling on an unconnected lane within the intersection in a consistent manner; and ([0088] The first way-point confidence level 815A can be represented by a circle centered on the first way-point 820A, where the radius of the circle is indicative of the confidence (e.g., the smaller the circle's radius the higher the confidence and the larger the circle's radius the lower the confidence, the larger the circle's radius the higher the uncertainty and the smaller the circle's radius the lower the uncertainty). The variation in confidence level can arise from the real-world observation that objects can operate with at least some degree of variation (e.g., various driving styles, various biking styles, various walking speeds, etc.) [0093] the first predicted trajectory 805A (which is indicative of a right-hand turn at the intersection).)
calculating, by the processor, the predicted path most suitable for the determined information based on a plurality of prediction paths in a second area with a high degree of freedom, ([0088] By way of example, as shown in FIG. 8, the model 136 can determine and output a first way-point confidence level 815A for a first way-point 820A of the first predicted trajectory 805A. The first way-point confidence level 815A can be represented by a circle centered on the first way-point 820A, where the radius of the circle is indicative of the confidence (e.g., the smaller the circle's radius the higher the confidence and the larger the circle's radius the lower the confidence, the larger the circle's radius the higher the uncertainty and the smaller the circle's radius the lower the uncertainty). The variation in confidence level can arise from the real-world observation that objects can operate with at least some degree of variation (e.g., various driving styles, various biking styles, various walking speeds, etc.). Additionally, or alternatively, the model 136 can determine and output a second way-point confidence level 815B for a second way-point 820B of the second predicted trajectory 805B. The second way-point confidence level 815B can be represented by a circle centered on the second way-point 820B, where the radius of the circle is indicative of the confidence/uncertainty associated with that particular predicted future location of the object.)
wherein a degree of freedom includes a degree at which the objects do not follow a driving line (Fig. 5 object 302 predicted trajectory 504 [0076] The motion planning system 128 can generate a motion plan 134 for the vehicle 104 based at least in part on the output 406 (and/or other data indicative of the predicted trajectory 502, 504 of an object 202, 302)
and calculating, by the processor, an end point at which the object is determined to advance with the highest probability based on a driving path of the object or dynamics information among a plurality of end points from which the object advance from the intersection. ([0088] The first way-point confidence level 815A can be represented by a circle centered on the first way-point 820A, where the radius of the circle is indicative of the confidence (e.g., the smaller the circle's radius the higher the confidence)…the model 136 can determine and output a second way-point confidence level 815B for a second way-point 820B of the second predicted trajectory 805B)
Djuric does not expressly disclose but Hiramatsu discloses determining, by the processor, ([0030] The controller 2 can be realized by using a microcomputer including a CPU (a central processing unit) that is an example of a control unit, a memory (a main storage device), and an input/output unit. ) a risk during driving of the vehicle based on a predicted path of the object; ([0049] A process advances to step S02, and the controller 2 assumes or detects another vehicle that has a risk of coming into contact with the host-vehicle 20. Specifically, the intersecting point estimating unit 13 predicts the first other vehicle track 31 of the first other vehicle 21 from the position, the traveling direction and the speed of the first other vehicle 21. The intersecting point estimating unit 13 predicts first other vehicle tracks 31 of all other vehicles present around the host-vehicle 20 detected by the surrounding environment detecting unit 12. On the other hand, the intersecting point estimating unit 13 predicts the host-vehicle track 30.)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify Djuric with the teachings of Hiramatsu with a reasonable expectation of success by reducing the amount of information to be processed and suppress delays in driving support of a host-vehicle as taught by Hiramatsu ([0004]).
Djuric does not expressly disclose but Houshmand discloses generating, by the processor, a reference path having a gentlest curve form from a current location of the object to the end point; and ([0042] the trajectory generation component 232 to determine a smooth trajectory and/or to complete a smooth path.)
determining, by the processor, as the predicted path of object, a candidate path having a smallest error from the reference path among at least one candidate path derivable based on the dynamics information of the object. ([0061] the cost may be based on the distance to a nearest object and/or a speed and/or direction of travel of a nearest dynamic object. The cost may additionally or alternatively be based at least in part on a deviation cost that is based at least in part on a deviation of the position, heading, velocity, and/or curvature specified by the position in the multivariate space from the route or other reference point (e.g., a target lane) and/or a displacement along the route. )
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify Djuric with the teachings of Houshmand with a reasonable expectation of success by allowing the autonomous vehicle to send a request to teleoperations and receive the selected trajectory from teleoperations without needing to bring the autonomous vehicle to a stop as taught by Houshmand ([0013]).
Regarding claim 19, Djuric does not expressly disclose but Hiramatsu discloses The method of claim 18, further comprising calculating, by the processor, ([0030] The controller 2 can be realized by using a microcomputer including a CPU (a central processing unit) that is an example of a control unit, a memory (a main storage device), and an input/output unit. ) an end point at which the object exits the intersection based on a driving path of the object and dynamics information. ([0032] The intersecting point estimating unit 13 estimates an intersecting point P at which the host-vehicle track 30 and the first other vehicle track 31 intersect with each other… For example, the intersecting point estimating unit 13 predicts that the first other vehicle 21 would travel straight on a currently traveling lane without change at an intersecting point from the position, the traveling direction and the speed of the first other vehicle 21)
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filling date of the claimed invention to modify Djuric with the teachings of Hiramatsu with a reasonable expectation of success by reducing the amount of information to be processed and suppress delays in driving support of a host-vehicle as taught by Hiramatsu ([0004]).
Regarding claim 20, Djuric does not expressly discl