DETAILED ACTION
This non-final action is in response to the application filed 28 March 2025.
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 .
Priority
Claims 1-20 are pending having a filing date of 28 March 2028 and claiming domestic benefit as a continuation of PCT/CN2023/103770, filed 29 June 2023, and claiming foreign priority to Chinese Patent Application Number CN 202211214482.1, filed 30 September 2022.
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements (IDSs) submitted 28 March 2025, 18 April 2025, and 15 January 2026, comply with 37 C.F.R. 1.97. Accordingly, the IDSs have been considered by the examiner. The initialed copies of the 1449 forms are enclosed herewith.
Drawings
The drawings, filed 28 March 2025, are accepted by the examiner.
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.
Claims 1 and 14-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Patent Publication Number 2024/0051572 to Cheng et al. (hereafter Cheng).
As per claim 1, Cheng discloses [a] method of intelligent driving decision-making (see at least Cheng, Abstract), comprising:
determining a game object, and obtaining real-time status information of the game object (see at least Cheng, [0054]disclosing an intelligent driving system uses a sensor to detect a surrounding environment and a status of the system, such as navigation positioning information, road information, information about obstacles such as another vehicle and a pedestrian, position and posture information of the system, and motion status information, and precisely controls a driving speed and steering of a vehicle through a specific decision planning algorithm, thereby implementing self driving),
wherein the game object is an obstacle identified by an ego vehicle (see at least Cheng, [0062] disclosing that the game object screening unit 301 determines, based on vehicle information of an ego vehicle, obstacle information of an obstacle, and road condition information that are input by another upper-layer module such as a sensor system, a positioning module, or an environment perception module, whether the ego vehicle conflicts with the obstacle when traveling along a reference path, so as to classify obstacles into a game object and a non-game object. The game object is an obstacle that may conflict with the ego vehicle, and the non-game object is an obstacle for which it is impossible to conflict with the ego vehicle) and
having n road topologies, and n>2 (see at least Cheng, [0056] disclosing that the navigation module 20 may be an in-vehicle navigation system, a navigation application (application, APP) on an external terminal and is configured to provide a navigation route of the ego vehicle and road condition information such as lane markings, traffic lights, and a fork in the route <lane markings, either left hand or right hand lane, and a fork in the route interpreted as n road topologies > 2>);
determining an intent probability that the game object travels along each road topology of the n road topologies based on the real-time status information of the game object, to obtain n intent probabilities (see at least Cheng, [0054] <sensor to detect a surrounding environment and a status of the system interpreted as real-time status information of the game object>; [0055]; [0078] disclosing (4) right-of-way cost: In a game process, the two game parties tend to travel in a traveling sequence specified in a traffic rule. If a difference between a game policy and a traveling rule specified in right-of-way information is comparatively large, a comparatively large right-of-way cost is generated. The right-of-way cost is directly proportional to a degree of violating a regulation in traveling; [0079] disclosing (5) obstacle prior probability cost: In a game process, a decision making result of an obstacle tends to approach a prior probability of a corresponding behavior obtained through observation. If a deviation between a game policy and the prior probability is comparatively large, a comparatively large obstacle prior probability cost is generated. The prior probability of the obstacle is related to a game scenario. If the game scenario is game decision making of overtaking/yielding, the prior probability of the obstacle is a prior probability of overtaking. If the game scenario is game decision making of avoiding/not avoiding, the prior probability of the obstacle is a prior probability of avoiding);
constructing a game sampling space for the game object based on real-time status information of the ego vehicle and the real-time status information of the game object (see at least Cheng, [0057] disclosing the decision making module 30 is configured to: receive paths that are predicted by the prediction module 10 and along which the ego vehicle and another vehicle around the ego vehicle are to travel in a future time period, and the navigation route of the ego vehicle and the road condition information such as the lane markings, traffic lights, and fork in the route that are provided by the navigation module 20; and determine whether the ego vehicle conflicts with the obstacle when traveling along a predicted route (or the navigation route). If the ego vehicle does not conflict with the obstacle, the ego vehicle does not game with the obstacle, and a moving manner and running track are determined according to a specified rule. If the ego vehicle conflicts with the obstacle, a game result between the ego vehicle and each obstacle is calculated based on input data, and each obstacle is tagged with a behavior label such as yielding/overtaking or avoiding/following),
wherein the game sampling space comprises m game strategies, and m>1 (see at least Cheng, [0058] disclosing that the planning module 40 is configured to receive a decision making result that is output by the decision making module 30, and determine, based on the behavior label of each obstacle, to perform an action such as yielding/overtaking or avoiding/following on the obstacle, for example, the ego vehicle selects a lane or chooses whether to change a lane, whether to follow a vehicle, whether to detour, or whether to park);
calculating strategy costs of the m game strategies in each road topology of the n road topologies, to obtain n groups of strategy costs (see at least Cheng, [0073]; [0074] disclosing that Policy cost evaluation: A policy cost of each game policy calculated by the game decision making unit 302 is related to factors such as safety, comfort, passing efficiency, right of way, a probability of allowing an obstacle to pass, and a historical decision making manner. Therefore, during calculation of the policy cost of each game policy, a cost of each factor may be calculated, and then weighting calculation is performed on the cost of each factor, to obtain the policy cost of each game policy. In this application, the policy cost of each game policy is analyzed by using six factors: a safety cost, a comfort cost, a passing efficiency cost, a right-of-way cost, an obstacle prior probability cost, and a historical decision correlation cost; [0075]-[0080]),
wherein each group of strategy costs of the n groups of strategy costs comprises m strategy costs that are in a one-to-one correspondence with the m game strategies (see at least Cheng, [0091] disclosing that a policy cost of each game policy is quantitatively described by using a cost of each design factor. Costs mentioned in this application include a safety cost, a comfort cost, an efficiency cost, a prior probability cost of a game object, a right-of-way cost, and a historical decision making result correlation cost, and a total cost is a weighted sum of the six costs. A total benefit corresponding to each decision making policy pair in the policy space is calculated; [0092]-[0095]; [0098]-[0099]; [0103] );
determining n target strategy costs corresponding to a total cost that meets a preset condition based on the n intent probabilities and the n groups of strategy costs (see at least Cheng, [0081] disclosing that the policy feasible region generation: The game decision making unit 302 weights the foregoing six factor costs according to a specific rule to obtain the policy cost of each game policy, then performs properness evaluation and screening on all factors weighted on the policy cost of each game policy, and deletes a policy cost of a game policy that includes an improper factor, so as to obtain a policy cost of a proper game policy through screening, and use the proper game policy as a feasible region between the ego vehicle and the game object; [0082]-[0085]),
wherein the n target strategy costs are strategy costs of a target game strategy in all the road topologies of the n road topologies, and the target game strategy is one of the m game strategies (see at least Cheng, [0091]; [0092]; [0094]; [0095]; [0098] ; [0103]; [0105] disclosing that a final cost Total corresponding to a policy space point {1, 1.45} can be obtained by adding up the foregoing six costs,; [0106]; -[0107] disclosing that as the time difference to collision (TDTC=−1.65s) between the two vehicles is determined, it can be determined that the ego vehicle arrives at the collision point first. This policy point corresponds to a decision of overtaking by the ego vehicle. Then, calculation of the foregoing steps is performed on each action combination pair in Table 1, so as to obtain total costs corresponding to all action combination pairs, as shown in Table; [0115]; [0116]; [0141]; [0142]); and
determining an outcome of decision-making of the ego vehicle based on the target game strategy (see at least Cheng, [0082] disclosing that the rule-based decision making unit 303 is configured to estimate a feasible region of the non-game object. In this application, to handle a problem of a decision making result conflict between the non-game object and the game object, a feasible region of the ego vehicle for a constraint area constituted by the non-game object should be estimated based on the constraint area).
As per claim 14, Cheng further discloses the following limitations:
wherein determining the n target strategy costs corresponding to the total cost that meets the preset condition comprises: respectively using the n intent probabilities as weights of the n road topologies, and calculating a weighted strategy cost corresponding to each game strategy in the n groups of strategy costs, to obtain m total costs (see at least Cheng, [0027] disclosing that the processing unit is configured to: determine all factors of the policy cost, where all the factors of the policy cost include at least one of safety, comfort, passing efficiency, right of way, a prior probability of an obstacle, and historical decision correlation; calculate a factor cost of each factor in each policy cost; and weight the factor cost of each factor in each policy cost, to obtain the policy cost of each game policy; ; [0054]; [0055]; [0075]-[0080]; [0092]-[0099]; [0141] disclosing Step S1507: Calculate a policy cost of each game policy, where the policy cost is a numerical value obtained by performing weighting on each factor weight that affects the policy cost.; [0142]);
determining a total cost with a minimum value from the m total costs (see at least Cheng, [0091] disclosing that a policy cost of each game policy is quantitatively described by using a cost of each design factor. Costs mentioned in this application include a safety cost, a comfort cost, an efficiency cost, a prior probability cost of a game object, a right-of-way cost, and a historical decision making result correlation cost, and a total cost is a weighted sum of the six costs); and
determining the n target strategy costs corresponding to the total cost with the minimum value (see at least Cheng, [0142] disclosing that Factors affecting the policy cost include safety, comfort, passing efficiency, right of way, a probability of allowing an obstacle to pass, a historical decision making manner, and the like. Therefore, during calculation of the policy cost of each game policy, a cost of each factor may be calculated, and then weighting calculation is performed on the cost of each factor, to obtain the cost of each game policy; [0144] disclosing Step S1509: Determine a decision making result of the ego vehicle, where the decision making result is a game policy with a smallest policy cost in same game policies in all sampling game spaces).
As per claim 15, Cheng further discloses the following limitations:
wherein calculating the strategy costs of the m game strategies in each road topology of the n road topologies comprises: determining factors of the strategy costs of the m game strategies in each road topology of the n road topologies (see at least Cheng, [0016]; [0073] disclosing that the game policy is changing an acceleration. The game decision making unit 302 determines, based on received data such as distances from the ego vehicle and the game object to a theoretical collision location, maximum and minimum acceleration values of the vehicle, the speed of the ego vehicle, and a maximum speed limit of a road, different types of game policies obtained by changing acceleration values of the ego vehicle and the game object, and uses a set of the game policies as the game policy range. Then, n acceleration values of the ego vehicle and m acceleration values of the game object are selected in a specified sampling manner, to obtain nxm possible combined game policy spaces of the two parties; [0074]),
wherein the factors of the strategy costs comprise at least one of safety, comfort, trafficability, a right of way, a horizontal offset, a risk area, or an inter-frame association (see at least Cheng, [0074] disclosing that a policy cost of each game policy calculated by the game decision making unit 302 is related to factors such as safety, comfort, passing efficiency, right of way, a probability of allowing an obstacle to pass, and a historical decision making manner; [0075]-[0080]);
calculating a factor cost of each factor in each strategy cost (see at least Cheng, [0074] disclosing that during calculation of the policy cost of each game policy, a cost of each factor may be calculated, and then weighting calculation is performed on the cost of each factor, to obtain the policy cost of each game policy. In this application, the policy cost of each game policy is analyzed by using six factors: a safety cost, a comfort cost, a passing efficiency cost, a right-of-way cost, an obstacle prior probability cost, and a historical decision correlation cost; [0075]-[0080]); and
weighting the factor cost of each factor in each strategy cost, to obtain the strategy costs of the m game strategies in each road topology of the n road topologies (see at least Cheng, [0074]; [0093]).
As per claim 16, Cheng further discloses the following limitations:
determining a decision-making upper limit and a decision-making lower limit of the ego vehicle and the game object based on the real-time status information of the ego vehicle and the real-time status information of the game object (see at least Cheng, [0012]; [0071] disclosing that the game decision making unit 302 determines upper decision limits and lower decision limits of game policies of two game parties based on a predefined game manner, the road condition information, and motion capabilities of the ego vehicle and the obstacle, to obtain a proper game decision range of the two game parties. Then, feasible game policy sampling is performed on the ego vehicle and the obstacle in the game policy range to obtain a quantity of feasible game policies of the two parties, and then the feasible game policies of the two parties are combined to obtain a plurality of different combined game policy spaces);
obtaining a decision-making strategy of the ego vehicle and a decision-making strategy of the game object from the decision-making upper limit and the decision-making lower limit according to a preset rule (see at least Cheng, [0089] disclosing that a longitudinal game policy between an ego vehicle and a game object may be represented by a magnitude of acceleration/deceleration (overtaking/yielding). First, an upper decision limit and a lower decision limit of the game policy (acceleration) are generated. The upper decision limit and the lower decision limit are obtained based on longitudinal vehicle dynamics, kinematic constraints, and a relative location and speed relationship between the ego vehicle and the game object); and
combining the decision-making strategy of the ego vehicle and the decision-making strategy of the game object, to obtain the m game strategies (see at least Cheng, [0087] disclosing that As shown in FIG. 5, in a traffic scenario with a track conflict, a planned path of an ego vehicle (black-colored) conflicts with a predicted path of a social vehicle (gray-colored), that is, a collision may occur at an intersection point. An upper-layer module provides a planned reference path of the ego vehicle, a predicted track of the social vehicle, current speeds and accelerations of the ego vehicle and the social vehicle, and a distance from each of the ego vehicle and the social vehicle to the collision point. The ego vehicle needs to perform a longitudinal game, for example, perform overtaking/yielding <interpreted as m game strategies> on an obstacle, based on the foregoing information),
wherein the m game strategies belong to the game sampling space (see at least Cheng, [0089]).
As per claim 17, Cheng further discloses the following limitations:
wherein determining the game object comprises: obtaining all road topologies of the n road topologies in a range of a preset area around the ego vehicle (see at least Cheng, [0016] disclosing the calculating a policy cost of each game policy includes: determining all factors of the policy cost, where all the factors of the policy cost include at least one of safety, comfort, passing efficiency, right of way, a prior probability of an obstacle, and historical decision correlation; calculating a factor cost of each factor in each policy cost; and weighting the factor cost of each factor in each policy cost, to obtain the policy cost of each game policy);
obtaining obstacle information of at least one obstacle around the ego vehicle (see at least Cheng, [0146] disclosing that to handle a problem of a decision making result conflict between the non-game object and the game object, a feasible region of the ego vehicle for a constraint area constituted by the non-game object should be estimated for the constraint area. For example, for a longitudinal (along a road direction in which the ego vehicle travels) action game (for example, overtaking/yielding), a virtual wall is constructed through virtualization in front of the ego vehicle as an upper acceleration limit constraint; and for a transverse (perpendicular to the road direction in which the ego vehicle travels) action game, the ego vehicle uses, as a constraint, a maximum transverse deflection range constituted by the non-game object), and
calculating a road topology of the at least one obstacle based on the obstacle information and all the road topologies of the n road topologies (see at least Cheng, [0087] disclosing that as shown in FIG. 5, in a traffic scenario with a track conflict, a planned path of an ego vehicle (black-colored) conflicts with a predicted path of a social vehicle (gray-colored), that is, a collision may occur at an intersection point. An upper-layer module provides a planned reference path of the ego vehicle, a predicted track of the social vehicle, current speeds and accelerations of the ego vehicle and the social vehicle, and a distance from each of the ego vehicle and the social vehicle to the collision point. ; [0146]); and
determining, from the at least one obstacle, the obstacle having the n road topologies as the game object (see at least Cheng, [0089] disclosing that a longitudinal game policy between an ego vehicle and a game object may be represented by a magnitude of acceleration/deceleration (overtaking/yielding). First, an upper decision limit and a lower decision limit of the game policy (acceleration) are generated. The upper decision limit and the lower decision limit are obtained based on longitudinal vehicle dynamics, kinematic constraints, and a relative location and speed relationship between the ego vehicle and the game object).
As per claim 18, similar to claim 1, Cheng discloses [a] decision-making apparatus (see at least Cheng, Abstract), comprising:
at least one processor (see at least Cheng, [0158] disclosing a computing device, including a memory and a processor); and
a storage having instructions stored therein, which when executed by the at least one processor (see at least Cheng, [0157] disclosing a computer-readable storage medium. The computer-readable storage medium stores a computer program. When the computer program is executed on a computer, the computer is enabled to perform any one of the foregoing method ), cause the decision-making apparatus to:
determine a game object, and obtain real-time status information of the game object (see at least Cheng, [0054]),
wherein the game object is an obstacle identified by an ego vehicle (see at least Cheng, [0062])
and having n road topologies, and n>2 (see at least Cheng, [0056]);
determine an intent probability that the game object travels along each road topology of the n road topologies based on the real- time status information of the game object, to obtain n intent probabilities (see at least Cheng, [0054]; [0055]; [0078]; [0079] );
construct game sampling space for the game object based on real-time status information of the ego vehicle and the real-time status information of the game object (see at least Cheng, [0057]),
wherein the game sampling space comprises m game strategies (see at least Cheng, [0058]); and
calculate strategy costs of the m game strategies in each road topology of the n road topologies, to obtain n groups of strategy costs (see at least Cheng, [0073]-[0080]),
wherein each group of strategy costs of the n groups of strategy costs comprises m strategy costs that are in a one-to-one correspondence with the m game strategies (see at least Cheng, [0091]-[0095]; [0098]-[0099]; [0103]);
determine n target strategy costs corresponding to a total cost that meets a preset condition based on the n intent probabilities and the n groups of strategy costs (see at least Cheng, [0081]-[0085]),
wherein the n target strategy costs are strategy costs of a target game strategy in all the road topologies of the n road topologies, and the target game strategy is one of the m game strategies (see at least Cheng, [0091]; [0092]; [0094]; [0095]; [0098] ; [0103]; [0105]-[0107]; [0115]; [0116]; [0141]; [0142]) ; and
determine an outcome of decision-making of the ego vehicle based on the target game strategy (see at least Cheng, [0082]; [0087]).
As per claim 19, similar to claims 1 and 18, Cheng discloses [a] method of vehicle traveling control (see at least Cheng, Abstract), comprising:
obtaining an obstacle outside a vehicle (see at least Cheng, [0054]; [0062]);
for the obstacle, determining an outcome of decision-making for traveling of the vehicle according to an intelligent driving decision-making method (see at least Cheng, [0087]); and
controlling the traveling of the vehicle based on the outcome of decision-making (see at least Cheng, [0087]);
wherein the intelligent driving decision-making method comprises: determining a game object, and obtaining real-time status information of the game object (see at least Cheng, [0054]; [0062]),
wherein the game object is an obstacle identified by an ego vehicle (see at least Cheng, [0062]; [0087])
and having n road topologies, and n>2 (see at least Cheng, [0056]);
determining an intent probability that the game object travels along each road topology of the n road topologies based on the real-time status information of the game object, to obtain n intent probabilities (see at least Cheng, [0054]; [0055]; [0078]; [0079] );
constructing game sampling space for the game object based on real-time status information of the ego vehicle and the real-time status information of the game object (see at least Cheng, [0057]),
wherein the game sampling space comprises m game strategies, and m>1 (see at least Cheng, [0058]);
calculating strategy costs of the m game strategies in each road topology of the n road topologies, to obtain n groups of strategy costs (see at least Cheng, [0073]-[0080]),
wherein each group of strategy costs of the n groups of strategy costs comprises m strategy costs that are in a one-to-one correspondence with the m game strategies (see at least Cheng, [0091]-[0095]; [0098]-[0099]; [0103]);
determining n target strategy costs corresponding to a total cost that meets a preset condition based on the n intent probabilities and the n groups of strategy costs (see at least Cheng, [0081]-[0085]),
wherein the n target strategy costs are strategy costs of a target game strategy in all the road topologies of the n road topologies, and the target game strategy is one of the m game strategies (see at least Cheng, [0091]; [0092]; [0094]; [0095]; [0098] ; [0103]; [0105]-[0107]; [0115]; [0116]; [0141]; [0142]); and
determining an outcome of decision-making of the ego vehicle based on the target game strategy (see at least Cheng, [0082]; [0087]).
Claim Rejections - 35 USC § 103
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 non-obviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 2, 6-8, 10-13 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Cheng as applied to claims 1 and 19 above, and further in view of U.S. Patent Publication Number 2023/0071224 to Garcia et al. (hereafter Garcia).
As per claim 2, Cheng discloses all of the limitations of claim 1, as shown above. Cheng further discloses the following limitation:
wherein determining the intent probability that the game object travels along each road topology of the n road topologies comprises: obtaining feature reference information of the game object in each road topology of the n road topologies (see at least Cheng, [0063] disclosing that, the vehicle information of the ego vehicle includes a navigation route provided by a navigation module 20 of the ego vehicle or a navigation device on an external terminal, and data such as a speed, an acceleration, a heading angle, and a location of the ego vehicle that are detected by each sensor in the vehicle. The obstacle information includes a location of each obstacle, a distance between obstacles, a distance between each obstacle and the ego vehicle, a type of each obstacle, a status of each obstacle, historical tracks of the ego vehicle and each obstacle, data such as a predicted traveling track and motion status in a future time period, and data such as a speed, an acceleration, and a heading angle of each obstacle. The road condition information includes traffic light information, road sign indication information; [0064] disclosing that after obtaining the vehicle information of the ego vehicle, the obstacle information, and the road condition information, the game object screening unit 301 determines, based on whether traveling tracks of the ego vehicle and each obstacle intersect each other, or based on data such as traveling tracks, speeds, and accelerations of the ego vehicle and each obstacle, whether there is an obstacle whose location is the same as the location of the ego vehicle (or a distance between locations of an obstacle and the ego vehicle is less than a specified threshold)) ... . But, the difference between Cheng and the claimed invention is that Cheng does not explicitly teach the following limitations taught in Garcia, a comparable method where it is known to add:
wherein the feature reference information is used to describe a datum status of an intent of the game object to travel along each road topology of the n road topologies in a current traffic environment status (see at least Garcia, [0046] disclosing that prediction service 122 on autonomous vehicle 102 may provide faster predictions for objects on the road in real time or near real time, such that the autonomous vehicle 102 may detect object positions within the lane, apply the one or more models to determine future behavior of the object, and affect the behavior of the autonomous vehicle 102 based on threshold probabilities that an object will perform a specific maneuver (e.g., drive cautiously around a vehicle drifting in a lane because of a probability being above a threshold that it will merge into the autonomous vehicle's 102 lane—even if there is no turn signal activated on the other vehicle). In some embodiments, prediction service 122 on autonomous vehicle 102 may utilize probabilistic determinations to emulate human driven vehicles on the road; [0053] disclosing that the AV can track, based on data from the lidar sensor system, how many and/or the rate at which vehicles make a turn from specific origination lanes to specific destination lanes. The AV may additionally track a path of each vehicle or other object, such as positional information, positional information relative to semantic map features, temporal information, and the like. In some embodiments, the historical information can be a combination of manual and AV collection technique); and
determining the intent probability that the game object travels along each road topology of the n road topologies based on the real-time status information of the game object and the feature reference information (see at least Garcia, [0045] disclosing that the remote computing system 150 can also include a prediction service 122 that utilizes one or more models (e.g., heuristics, machine learned models, etc.) to analyze historical data 123. Historical data 123 can be data either received or captured via sensor system 1 104 . . . sensor system N 106 about objects proximate to the autonomous vehicle 102, such as other vehicles, bicycles, pedestrians, or other entities that are not controllable. The analysis of the historical data 123 can determine a probabilistic prediction of how these objects may behave and/or interact with autonomous vehicle 102 on the road. Map update service 160 takes the prediction from prediction service 122 and implements the statistical behavior of the objects at a mapped location (e.g., updates the map with associated statistical information that prediction service 122 can utilize to make predictions) ; [0061] disclosing that FIG. 4 shows an example visualization of historical object behavior in accordance with some aspects of the present technology. In this example, vehicles are tracked as they are making turns from an origination lane to a destination lane (e.g., which path the vehicle takes) at a specific intersection. Based on an analysis of how the vehicles turn, such as the rates at which vehicles turn into specific destination lane from each origination lane, statistical probabilities for each path can be determined and assigned. Subsequently, when a vehicle on the road approaches the intersection, that vehicle can be assigned a statistical probability for each potential path; [0066] disclosing that FIG. 6 shows an example visualization of historical and predicted object behavior in accordance with some aspects of the present technology. Intersection 630 includes lanes 602, 604, 606, 608, 610, 612, 614, 616, 618, 620, 622, 624, 626, and 628 and bike lanes 602, 614, and 622. In this example, the probabilistic rates for multiple turning lanes are shown, with legal turns shown in dashed lines and illegal turns shown in solid lines; [0070]; [0071]).
Cheng and Garcia are analogous art to claim 2 because they are in the same field of intelligent driving. Cheng relates to the field of intelligent driving technologies (see at least Cheng, [0002]). Garcia relates to predicting object behavior proximate to a navigating autonomous vehicle (see at least Garcia, [0002]).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method, as disclosed in Cheng, to provide the benefit of having the feature reference information be used to describe a datum status of an intent of the game object to travel along each road topology of the n road topologies in a current traffic environment status and determining the intent probability that the game object travels along each road topology of the n road topologies based on the real-time status information of the game object and the feature reference information, as disclosed in Garcia, with a reasonable expectation of success. The results would have been predictable to one of ordinary skill in the art.
As per claim 6, the combination of Cheng and Garcia disclose all of the limitations of claim 2, as shown above. Cheng further disclose the following limitations:
wherein the feature reference information is obtained based on a target road point in each road topology of the n road topologies (see at least Cheng, [0064] disclosing that after obtaining the vehicle information of the ego vehicle, the obstacle information, and the road condition information, the game object screening unit 301 determines, based on whether traveling tracks of the ego vehicle and each obstacle intersect each other <interpreted as the target road point in each road topology of the n roads>, or based on data such as traveling tracks, speeds, and accelerations of the ego vehicle and each obstacle, whether there is an obstacle whose location is the same as the location of the ego vehicle (or a distance between locations of an obstacle and the ego vehicle is less than a specified threshold).; [0065]; [0066] ); or
the feature reference information is obtained based on a road congestion degree or a radical degree of a traveling style of the game object (see at least Cheng, [0116] disclosing that the overtaking/yielding decision making solution for an obstacle does not depend on a specific obstacle interaction form or a track intersection feature, but uses a sensor system to obtain traffic scenario information <interpreted as road congestion degree>, so as to properly abstract a traffic scenario, thereby implementing application scenario generalization).
As per claim 7, the combination of Cheng and Garcia disclose all of the limitations of claim 2, as shown above. Cheng further disclose the following limitations:
wherein the real-time status information of the game object comprises one or more of the following: real-time position information of the game object, a real-time orientation angle of the game object, a real-time speed of the game object, or a real-time acceleration of the game object (see at least Cheng, [0054] disclosing an intelligent driving system uses a sensor to detect a surrounding environment and a status of the system, such as navigation positioning information, road information, information about obstacles such as another vehicle <interpreted as real time speed or angle or acceleration of the game object>and a pedestrian, position and posture information of the system, and motion status information, and precisely controls a driving speed and steering of a vehicle through a specific decision planning algorithm, thereby implementing self driving; [0062] disclosing that he game object screening unit 301 determines, based on vehicle information of an ego vehicle, obstacle information of an obstacle, and road condition information that are input by another upper-layer module such as a sensor system, a positioning module, or an environment perception module, whether the ego vehicle conflicts with the obstacle when traveling along a reference path, so as to classify obstacles into a game object and a non-game object.; [0089] disclosing that a longitudinal game policy between an ego vehicle and a game object may be represented by a magnitude of acceleration/deceleration (overtaking/yielding). ); and
the feature reference information comprises one or more of reference position information of the game object, a reference orientation angle of the game object, a reference speed of the game object, or a reference acceleration of the game object (see at least Cheng, [0054]; [0062]; [0089]).
As per claim 8, the combination of Cheng and Garcia discloses all of the limitations of claim 7, as shown above. Cheng further discloses the following limitation:
wherein when no obstacle other than the ego vehicle exists around the game object a value of the reference speed of the game object is obtained based on a kinematic constraint on the game object or an environmental traffic constraint on the game object (see at least Garcia, [0087] disclosing that FIG. 9 shows an example embodiment applying the method of FIG. 8. In FIG. 9, an example visualization of current and predicted object behavior is shown in accordance with some aspects of the previous method. Autonomous vehicle 902 is navigating down road 906 with a specific object—in this case, vehicle 904 (in other embodiments, the specific object could be a bicycle, pedestrian, bus, etc.). Vehicle 904 is ahead of autonomous vehicle 902 in lane 910. Autonomous vehicle 902 is within lane 908; [0088] disclosing that sensors on autonomous vehicle 902 have tracked vehicle 904 as it travels down lane 910, and has captured that vehicle 904 has drifted from the middle of lane 910 to the left side of lane 910.; [0092] disclosing that the autonomous vehicle 102 can monitor (1004) a velocity of the first vehicle as it approaches the intersection. The autonomous vehicle 102 can monitor the velocity, for example, through sensor system 104-106 as the first vehicle approaches the intersection. The velocity can be used to predict the probability that the first vehicle will yield at a specific location based on historical data 206 that indicates past vehicles stopping at the specific location with similar velocities. The autonomous vehicle 102 can determine that the first vehicle is yielding to the autonomous vehicle 102 when the autonomous vehicle 102 detects that the first vehicle has stopped proximate to the average yield location for the average yield time as indicated by one or more models ).
As per claim 10, the combination of Cheng and Garcia discloses all of the limitations of claim 7, as shown above.
wherein when at least one first target obstacle located in front of the game object exists in the n road topologies, and a distance between the game object and the at least one first target obstacle is shorter than a preset safe distance (as cited in claim 6, see at least Cheng, [0064]; [0065]; [0066]) ... . Garcia further disclose the following limitation:
the reference speed of the game object is obtained based on a speed of the at least one first target obstacle (see at least Garcia, [0046]; [0092] disclosing that the autonomous vehicle 102 can monitor (1004) a velocity of the first vehicle as it approaches the intersection. The autonomous vehicle 102 can monitor the velocity, for example, through sensor system 104-106 as the first vehicle approaches the intersection. The velocity can be used to predict the probability that the first vehicle will yield at a specific location based on historical data 206 that indicates past vehicles stopping at the specific location with similar velocities. The autonomous vehicle 102 can determine that the first vehicle is yielding to the autonomous vehicle 102 when the autonomous vehicle 102 detects that the first vehicle has stopped proximate to the average yield location for the average yield time as indicated by one or more models ).
As per claim 11, the combination of Cheng and Garcia disclose all of the limitations of claim 7, as shown above. Cheng further disclose the following limitation:
wherein when at least one second target obstacle exists in a traveling direction that is the same as a traveling direction of the game object, and a road topology owned by the at least one second target obstacle does not belong to the n road topologies, the reference speed of the game object is obtained based on a datum speed of the at least one second target obstacle (see at least Cheng, Fig. 3, showing Scenario 3, showing a game objection, game object 1 and game object 2 <interpreted as the second target obstacle exists in a traveling direction that is the same as a traveling direction of the game object >; [0063] disclosing that obstacle information includes a location of each obstacle, a distance between obstacles, a distance between each obstacle and the ego vehicle, a type of each obstacle, a status of each obstacle, historical tracks of the ego vehicle and each obstacle, data such as a predicted traveling track and motion status in a future time period, and data such as a speed, an acceleration, and a heading angle of each obstacle; [0068] disclosing scenario 3: Multi-obstacle decision making (there are a plurality of game objects): For example, the ego vehicle goes straight, and the plurality of obstacles cross a planned path of the ego vehicle) ... . Garcia further discloses the following limitation:
the reference speed of the game object is obtained based on a datum speed of the at least one second target obstacle (see at least Garcia, [0046] disclosing that The internal computing system 110 on the autonomous vehicle 102 can also include prediction service 122 that can similarly determine a probabilistic prediction of how these objects may behave and/or interact with autonomous vehicle 102 on the road based on analysis of the historical data 123. In some embodiments, prediction service 122 on autonomous vehicle 102 may provide faster predictions for objects on the road in real time or near real time, such that the autonomous vehicle 102 may detect object positions within the lane, apply the one or more models to determine future behavior of the object, and affect the behavior of the autonomous vehicle 102 based on threshold probabilities that an object will perform a specific maneuver (e.g., drive cautiously around a vehicle drifting in a lane because of a probability being above a threshold that it will merge into the autonomous vehicle's 102 lane—even if there is no turn signal activated on the other vehicle); [0092] disclosing that the autonomous vehicle 102 can monitor (1004) a velocity of the first vehicle as it approaches the intersection. The autonomous vehicle 102 can monitor the velocity, for example, through sensor system 104-106 as the first vehicle approaches the intersection. The velocity can be used to predict the probability that the first vehicle will yield at a specific location based on historical data 206 that indicates past vehicles stopping at the specific location with similar velocities. The autonomous vehicle 102 can determine that the first vehicle is yielding to the autonomous vehicle 102 when the autonomous vehicle 102 detects that the first vehicle has stopped proximate to the average yield location for the average yield time as indicated by one or more models).
As per claim 12, the combination of Cheng and Garcia disclose all of the limitations of claim 7, as shown above. Cheng further disclose the following limitation:
wherein when at least one third target obstacle exists in a crossing traveling direction of the game object, and a difference between moments at which the game object and the at least one third target obstacle reach a predicted collision point is greater than a preset threshold (see at least Cheng, [0064] disclosing that after obtaining the vehicle information of the ego vehicle, the obstacle information, and the road condition information, the game object screening unit 301 determines, based on whether traveling tracks of the ego vehicle and each obstacle intersect each other, or based on data such as traveling tracks, speeds, and accelerations of the ego vehicle and each obstacle, whether there is an obstacle whose location is the same as the location of the ego vehicle (or a distance between locations of an obstacle and the ego vehicle is less than a specified threshold); [0130] disclosing that with regard to FIG. 4, the ego vehicle performs comprehensive decision making based on all considered obstacles, to obtain an optimal game result that can satisfy a plurality of obstacles at the same time. When a vehicle on a planned path of the ego vehicle constitutes a virtual-wall constraint on the ego vehicle, the vehicle corresponds to a feasible region whose acceleration range is [−4.0, 0.8]. In a game process, the ego vehicle performs game decision making on the two social vehicles A and B separately <vehicle B interpreted as at least one second target vehicle traveling in a direction that is the same as that of the game object>, so as to obtain a feasible region of the ego vehicle for each vehicle. The intersection of the three feasible regions is calculated, to obtain the feasible region that satisfies all the obstacles in the scenario. The optimal solution of the ego vehicle for all the game obstacles is obtained in the feasible region, thereby obtaining a result that the ego vehicle needs to yield to both the two obstacles, namely, the social vehicles A and B) ... . Garcia further discloses the following limitation:
the reference speed of the game object is obtained based on a datum speed of the at least one second target obstacle (see at least Garcia, [0046]; [0092]).
As per claim 13, the combination of Cheng and Garcia disclose all of the limitations of claim 7, as shown above. Cheng further disclose the following limitations:
wherein when at least one third target obstacle exists in a crossing traveling direction of the game object, and a difference between moments at which the game object and the at least one third target obstacle reach a predicted collision point is less than or equal to a preset threshold (see at least Cheng, Figure 3, scenario 1, showing obstacle in the cross traveling direction of the game object; [0064]; [0066] disclosing Scenario 1: Single-obstacle decision making (there is a point at which tracks intersect <interpreted as reaching a predicted collision point>): For example, the ego vehicle goes straight, and the obstacle goes across) ... . Garcia further discloses the following limitations:
the reference speed of the game object is obtained based on the real-time speed of the game object, and the reference speed is lower than the real-time speed of the game object (see at least Garcia, [0046]; [0092] ).
As per claim 20, similar to claim 2, Cheng discloses all of the limitations of claim 19, as shown above. Cheng further discloses the following limitation:
wherein determining the intent probability that the game object travels along each road topology of the n road topologies comprises: obtaining feature reference information of the game object in each road topology of the n road topologies (see at least Cheng, [0063]; [0064]) ... . But, the difference between Cheng and the claimed invention is that Cheng does not explicitly teach the following limitations taught in Garcia, a comparable method where it is known to add:
wherein the feature reference information is used to describe a datum status of an intent of the game object to travel along each road topology of the n road topologies in a current traffic environment status (see at least Garcia, [0046]; [0053]); and
determining the intent probability that the game object travels along each road topology of the n road topologies based on the real-time status information of the game object and the feature reference information (see at least Garcia, [0045]; [0061]; [0066]; [0070]; [0071]).
Cheng and Garcia are analogous art to claim 20 because they are in the same field of intelligent driving. Cheng relates to the field of intelligent driving technologies (see at least Cheng, [0002]). Garcia relates to predicting object behavior proximate to a navigating autonomous vehicle (see at least Garcia, [0002]).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method, as disclosed in Cheng, to provide the benefit of having the feature reference information be used to describe a datum status of an intent of the game object to travel along each road topology of the n road topologies in a current traffic environment status and determining the intent probability that the game object travels along each road topology of the n road topologies based on the real-time status information of the game object and the feature reference information, as disclosed in Garcia, with a reasonable expectation of success. The results would have been predictable to one of ordinary skill in the art.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Cheng and Garcia as applied to claim 2 above, and further in view of U.S. Patent Publication Number 2022/0390607 to Du et al. (hereafter Du).
As per claim 3, the combination of Cheng and Garcia discloses all of the limitations of claim 2, as shown above. Cheng further discloses the following limitations:
wherein determining the intent probability that the game object travels along each road topology of the n road topologies based on the real-time status information of the game object and the feature reference information comprises:
obtaining ... (1) ... based on the real-time status information of the game object and the feature reference information (see at least Cheng, [0057]; [0063]);
... (2) ... and
determining the intent probability that the game object travels along each road topology of the n road topologies based on the likelihood probability (see at least Cheng, [0063]; [0081]). But, the difference between the combination and the claimed invention is that neither Cheng nor Garcia explicitly teach the following limitations taught in Du, a comparable method where it was known to have:
(1) obtaining a target eigenvector based on the real-time status information and the feature reference information (see at least Du, [0019] disclosing that the LIDAR-to-vehicle alignment process further includes, while determining the ground-truth positions: based on a vehicle speed, a type of acceleration maneuverer and a global positioning system signal strength, assigning weights to the points of data to indicate confidence levels in the points of data; removing ones of the points of data having weight values less than a predetermined weight; and determining a model of a feature corresponding to remaining ones of the points of data using principal component analysis to generate the ground truth-data, where the model is of a plane or a line, where the ground-truth data includes the model, an eigenvector, and a mean vector ) ... ; and
(2) determining a likelihood probability of the target eigenvector based on a probability density distribution dataset comprising a probability density distribution of eigenvectors with different eigenvector values (see at least Du, [0107] disclosing that at 1008, a LIDAR point cloud registration (e.g., an iterative closest point (ICP) algorithm and/or a generalized iterative closest point (GICP)) algorithm is performed to find a transformation between current data and ground-truth data. A weight may be used in an ICP and/or GICP optimization function. ; [0108] disclosing that ICP is an algorithm used to minimize a difference between two point clouds. ICP may include computing correspondences between two scans and computing a transformation, which minimizes distance between corresponding points. Generalized ICP is similar to ICP and may include attaching a probabilistic model to a minimization operation of ICP. The ICP algorithm may perform a rigid registration in an iterative fashion by alternating between (i) given the transformation, finding the closest point in S for every point in M, and (ii) given the correspondences, finding the best rigid transformation by solving a least squares problem. Point set registration is the process of aligning two points sets) ... .
Cheng, Garcia and Du are analogous art to claim 9 because they are in the same field of intelligent driving. Cheng relates to the field of intelligent driving technologies (see at least Cheng, [0002]). Garcia relates to predicting object behavior proximate to a navigating autonomous vehicle (see at least Garcia, [0002]). Du elates to vehicle object detection systems (see at least Du, [0002]).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method, as disclosed in Cheng, as modified by Garcia, to provide the benefit of obtaining a target eigenvector based on the real-time status information and the feature reference information, and determining a likelihood probability of the target eigenvector based on a probability density distribution dataset comprising a probability density distribution of eigenvectors with different eigenvector values, as disclosed in Du, with a reasonable expectation of success. The results would have been predictable to one of ordinary skill in the art.
Claims 4 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Cheng, Garcia and Du as applied to claim 3 above, and further in view of Chinese Patent Publication Number CN 113971752 A to Chen et al. (hereafter Chen).
As per claim 4, the combination of Chen, Garcia and Du discloses all of the limitations of claim 3, as shown above. But, neither Chen, Garcia, nor Du explicitly teach the following limitation taught in Chen:
wherein the probability density distribution dataset comprises:
a nonlinear probability density distribution dataset comprising a probability density distribution of eigenvectors with different speed values and different eigenvector values (see at least Chen, pg. 2, para. 1, disclosing that the non-linear optimal filtering is widely applied to multi-source information fusion, which can be uniformly described by the recursive Bayesian method. The core idea is to calculate the posterior probability density function of the nonlinear system state vector based on the obtained observed value. for the linear system, the closed solution of the optimal Bayesian filtering is described by the Kalman filtering equation).
Cheng, Garcia, Du and Chen are analogous art to claim 4 because they are in the same field of intelligent driving. Cheng relates to the field of intelligent driving technologies (see at least Cheng, [0002]). Garcia relates to predicting object behavior proximate to a navigating autonomous vehicle (see at least Garcia, [0002]). Du elates to vehicle object detection systems (see at least Du, [0002]). Chen relates to a multi-vehicle cooperative target vehicle tracking method for anti-observation data interference (see Chen, pg. 1, para. 2).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method, as disclosed in Cheng, as modified by Garcia, to provide the benefit of having the probability density distribution dataset comprise a nonlinear probability density distribution dataset comprising a probability density distribution of eigenvectors with different speed values and different eigenvector values, as disclosed in Chen, with a reasonable expectation of success. Doing so would provide the benefit of improving the precision of the target vehicle state estimation (see at least Chen, pg. 2, para. 3 ).
As per claim 5, the combination of Chen, Garcia and Du discloses all of the limitations of claim 3, as shown above. Cheng further disclose the following limitations:
wherein determining the intent probability that the game object travels along each road topology of the n road topologies based on the likelihood probability comprises: obtaining a prior intent probability that the obstacle travels along each road topology of the n road topologies (see at least Cheng, [0079] disclosing an Obstacle prior probability cost: In a game process, a decision making result of an obstacle tends to approach a prior probability of a corresponding behavior obtained through observation. If a deviation between a game policy and the prior probability is comparatively large, a comparatively large obstacle prior probability cost is generated. The prior probability of the obstacle is related to a game scenario. If the game scenario is game decision making of overtaking/yielding, the prior probability of the obstacle is a prior probability of overtaking. If the game scenario is game decision making of avoiding/not avoiding, the prior probability of the obstacle is a prior probability of avoiding); and
calculating the intent probability that the game object travels along each road topology of the n road topologies based on the likelihood probability (see at least Cheng, [0098] disclosing that The prior probability cost of the game object is related to a probability that the game object allows the ego vehicle to pass. A larger probability of yielding indicates a smaller prior probability cost of the game object. A quantized relationship is shown in FIG. 10. The yielding probability of the game object reflects a driving style of the game object, and is a dynamic factor that depends on information such as a historical speed, an acceleration, and a location of the game object. The yielding probability of the game object is an input of a game module. In the scenario described above, if the yielding probability of the game object is 0.2, a corresponding eProb cost of overtaking by the ego vehicle is (1-0.2)*1000=800, where 1000 is a prior probability cost weigh ) ... . But, neither Chen, Garcia, nor Du explicitly teach the following limitation taught in Chen:
the prior intent probability using a Bayesian inference algorithm (as cited for claim 4, see at least Chen, pg. 2, para. 1).
Cheng, Garcia, Du and Chen are analogous art to claim 5 because they are in the same field of intelligent driving. Cheng relates to the field of intelligent driving technologies (see at least Cheng, [0002]). Garcia relates to predicting object behavior proximate to a navigating autonomous vehicle (see at least Garcia, [0002]). Du elates to vehicle object detection systems (see at least Du, [0002]). Chen relates to a multi-vehicle cooperative target vehicle tracking method for anti-observation data interference (see Chen, pg. 1, para. 2)
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method, as disclosed in Cheng, as modified by Garcia, to provide the benefit of having the prior intent probability use a Bayesian inference algorithm, as disclosed in Chen, with a reasonable expectation of success. Doing so would provide the benefit of improving the precision of the target vehicle state estimation (see at least Chen, pg. 2, para. 3).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Cheng and Garcia as applied to claim 8 above, and further in view of U.S. Patent Publication Number 2011/0190972 to Timmons et al. (hereafter Timmons).
As per claim 9, the combination of Cheng and Garcia discloses all of the limitations of claim 8, as shown above. But, the difference between the combination and the claimed invention is that neither Cheng nor Garcia explicitly teach the following limitations taught in Timmons, a comparable method where it was known to have:
wherein the kinematic constraint comprises one or more of the following: a lane topology curvature speed limit, a start acceleration constraint, or a red-light deceleration constraint (see at least Timmons, [0156] disclosing that the multiple feature ACC is an autonomous and convenience feature that extends the conventional ACC by integrating multiple features including conventional cruise control, ACC, speed-limit following, and curve speed control.; [0157] disclosing that conventional cruise control maintains vehicle speed at the driver-selected reference or set speed v.sub.SET, if there is no preceding vehicle or curve or speed-limit change. The monitored input to the conventional cruise control is vehicle speed. The speed controller calculates necessary acceleration command a.sub.cmd. If the acceleration command is positive, throttle is applied, and if the acceleration command is negative, brake is applied; [0161] disclosing that Speed limit following (SLF) automatically changes the set speed in response to detected changes in the legal speed limit. In one exemplary embodiment, a system equipped with SLF reduces vehicle speed before entering into a lower speed-limit zone and accelerates after entering the higher speed-limit zone; [0162] ); and
the environmental traffic constraint comprises one or more of the following: a road speed limit of a lane in which the game object is located, a speed constraint on a preceding vehicle in the lane in which the game object is located, or a speed constraint on an obstacle in a lane other than the lane in which the game object is located (see at least Timmons, [0156]; [0157] disclosing that conventional cruise control maintains vehicle speed at the driver-selected reference or set speed v.sub.SET, if there is no preceding vehicle or curve or speed-limit change. The monitored input to the conventional cruise control is vehicle speed. The speed controller calculates necessary acceleration command a.sub.cmd. If the acceleration command is positive, throttle is applied, and if the acceleration command is negative, brake is applied ; [0160] disclosing that the monitored inputs are vehicle speed, range and range rate. The ACC Command Generation block generates desired speed v.sub.ACC and desired acceleration a.sub.ACC. The speed controller calculates necessary acceleration command a.sub.cmd as an output, and outputs the command to a vehicle speed control system. If the acceleration command is positive, throttle is applied, and if the acceleration command is negative, brake is applied ; [0178] disclosing that methods are known to utilize information regarding the driving environment around a vehicle to control autonomously or semi-autonomously the relative location of the vehicle with respect to a lane and with respect to other vehicles. FIG. 54 depicts vehicles utilizing exemplary methods to control vehicle operation, in accordance with the present disclosure. Vehicle 3105, vehicle 3205, and vehicle 3305 are traveling in lane 300 defined by lane markers 305A and 305B. Vehicle 3205 is utilizing a radar signal to determine a range to vehicle 3105, useful, for example, in an ACC application, and vehicle 3205 is additionally utilizing known methods to establish an estimated position within the lane and determine lane keeping boundaries 325A and 325B.).
Cheng, Garcia and Timmons are analogous art to claim 9 because they are in the same field of intelligent driving. Cheng relates to the field of intelligent driving technologies (see at least Cheng, [0002]). Garcia relates to predicting object behavior proximate to a navigating autonomous vehicle (see at least Garcia, [0002]). Timmons relates to systems for detecting the presence of stationary and non-stationary objects in the vicinity of a traveling vehicle, and controlling vehicle operational parameters in response to the presence of such objects (see at least Timmons, [0001]).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method, as disclosed in Cheng, as modified by Garcia, to provide the benefit of having the kinematic constraint comprises one or more of the following: a lane topology curvature speed limit, a start acceleration constraint, or a red-light deceleration constraint, as disclosed in Timmons, with a reasonable expectation of success. The results would have been predictable to one of ordinary skill in the art.
Conclusion
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PATRICK M. BRADY III
Examiner
Art Unit 3665
/PATRICK M BRADY/Examiner, Art Unit 3665
/Erin D Bishop/Supervisory Patent Examiner, Art Unit 3665
Prosecution Insights