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 .
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.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). Receipt is acknowledged of a certified copy of foreign application CN202410865869.6, as required by 37 CFR 1.55.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 13 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter.
Claim 13 recites “A computer readable storage medium.” The computer readable medium is an example of: Transitory forms of signal transmission (often referred to as "signals per se"), such as a propagating electrical or electromagnetic signal or carrier wave; (See MPEP 2106.03). Furthermore, the BRI of machine-readable media can encompass non-statutory transitory forms of signal transmission, such as propagating electrical or electromagnetic signal per se. See In re Nuijten, 500 F.3d 1346, 84 USPQ2d 1495 (Fed. Cir. 2007). When the broadest reasonable interpretation encompasses transitory forms of signal transmission, a rejection under 35 U.S.C. 101 as failing to claim statutory subject matter would be appropriate. Thus, a claim to a computer readable medium that can be a compact disc or a carrier wave covers a non-statutory embodiment and therefore should be rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See, e.g., Mentor Graphics v. EVE-USA, Inc., 851 F.3d at 1294-95, 112 USPQ2d at 1134 (claims to a "machine-readable medium" were non-statutory, because their scope encompassed both statutory random-access memory and non-statutory carrier waves)(See MPEP 2106.03).
The Examiner suggests that the Applicant replace the term “A computer readable storage medium” with the term “A non-transitory computer readable storage medium” to the medium as recited in the claim(s) in order to properly render the claim(s) in statutory form in view of their broadest reasonable interpretation in light of the originally filed specification. Applicant is suggested to review page 4 of the and Interim Examination Instructions for Evaluating Subject Matter Eligibility Under 35 U.S.C. § 101, Aug. 24, 2009, under section II. Subsection (c), which describes a “non-transitory computer readable storage medium” being patent-eligible subject matter.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3, 6, 8, 13-16, 19-20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 12304530 B1 Lorenzetti; Joseph et al. (hereinafter Lorenzetti).
Regarding claim 1, Lorenzetti discloses: A method for controlling a vehicle (see Lorenzetti at least [col. 1, lines 42-45] methods, systems, and computer-readable media for controlling how a vehicle may be controlled to determine and minimize a likelihood of collision between it and other road users), comprising:
determining first ego vehicle state information of the vehicle at a current time point and first obstacle state information of an obstacle around the vehicle at the current time point (see Lorenzetti at least [col. 15, line 34] receiving a trajectory for a vehicle and [col. 15, line 41-42] receiving a trajectory for an agent);
determining, based on the first ego vehicle state information, first position probability distribution information of the vehicle at a future time point (see Lorenzetti at least [col. 3, lines 41-43] The probability distributions may be determined or defined relative to a position of the vehicle or agent along its trajectory at the time step being considered and [col. 15, lines 62-64] determining a vehicle probability distribution for the vehicle at the first time along its trajectory);
determining, based on the first obstacle state information, second position probability distribution information of the obstacle at the future time point (see Lorenzetti at least [col. 15, lines 66-67] determining an agent probability distribution for the agent at the first time along its trajectory);
determining, based on the first position probability distribution information and the second position probability distribution information, collision indication vector distribution information between the obstacle and the vehicle at the future time point (see Lorenzetti at least [col. 4, lines 17-24] to determine a probability of collision, the combined distribution may be analyzed over a relative velocity or trajectory between the agent and the vehicle in the relative positional space. The relative velocity or trajectory may comprise a vector);
determining, based on the collision indication vector distribution information, a collision risk state between the vehicle and the obstacle (see Lorenzetti at least [col. 2, lines 38-43] By determining probability of collision, control of the vehicle to minimize the risk of collision may result in more desirable behaviors, such as selecting a trajectory that includes the vehicle decelerating, coming to a stop to avoid a collision, or taking a path that avoids the agent); and
controlling a driving state of the vehicle based on the collision risk state (see Lorenzetti at least [col. 9, lines 11-13] the probability of collision of the vehicle with the agent may be determined, and so that the vehicle can therefore be controlled accordingly).
Regarding claim 2, Lorenzetti discloses: The method according to claim 1, wherein determining, based on the collision indication vector distribution information, the collision risk state between the vehicle and the obstacle comprises:
determining a current road scenario type based on the first ego vehicle state information and the first obstacle state information (see Lorenzetti at least [col. 16, lines 22-29] The probability distributions may be based on a state of the agent or vehicle, as defined by one or more of the vehicle systems. The state of an agent may include information relating to at least one of an entity type, a direction of travel, a trajectory, a current position, a behavior profile, whether the agent is being controlled manually or autonomously, whether the agent is static or in motion, or environmental conditions);
determining a current collision region based on the current road scenario type (see Lorenzetti at least [col. 19, lines 38-43] In order to take account of a size of the vehicle and the agent, an area that may be occupied by the vehicle and agent together may be used to evaluate the combined distribution);
determining, based on the current collision region and the collision indication vector distribution information, a collision risk value at the future time point (see Lorenzetti at least [col. 19, lines 63-64] The region may be then used to determine a probability of collision over the time step and [col. 17, lines 8-11] The probability values may be used to determine a further value, which may represent an overall probability of collision or a cost value); and
determining, based on the collision risk value and a collision risk threshold, the collision risk state between the vehicle and the obstacle (see Lorenzetti at least [col. 6, lines 13-15] A cost value may be compared to a threshold value and action may be taken if the cost value is above the threshold value).
Regarding claim 3, Lorenzetti discloses: The method according to claim 2, wherein the determining, based on the current collision region and the collision indication vector distribution information, a collision risk value at the future time point comprises:
determining an integral value of collision indication vector distribution information in the current collision region, as the collision risk value corresponding to the future time point (see Lorenzetti at least [col. 19, lines 52-56] The velocity vector or trajectory may indicate a relative direction of travel, over which an area or dimension of the vehicle, agent, or combination may be integrated to determine a probability of collision over the area occupied by the vehicle and/or agent).
Regarding claim 6, Lorenzetti discloses: The method according to claim 2, wherein the determining a current collision region based on the current road scenario type comprises:
determining a first length and a first width of the vehicle, and a second length and a second width of the obstacle (see Lorenzetti at least [col. 4, line 67 – col. 5, line 2] one or more dimensions of the vehicle and/or agent, such as a width of the vehicle and/or agent, may be determined); and
determining the current collision region based on the current road scenario type, the first length, the first width, the second length, and the second width (see Lorenzetti at least [col. 5, lines 2-6] The dimension or dimensions may be used to determine a plurality of points around the relative position at which the probability of collision may be determined by evaluating the distribution or a function based thereon).
Regarding claim 8, Lorenzetti discloses: The method according to claim 1, wherein the determining, based on the first position probability distribution information and the second position probability distribution information, collision indication vector distribution information between the obstacle and the vehicle at the future time point comprises:
superposing first position probability distribution information and second position probability distribution information based on preset coefficients, to obtain collision indication vector distribution information corresponding to the future time point (see Lorenzetti at least [col. 8, lines 60-62] at box 134, there is some overlap between the ellipses of vehicle 102 and the agent 128 at time t.sub and [col. 8, line 67 – col. 9, line 8] box 134 shows two separate probability distributions 146, 148 representing likely positions of the vehicle 102 and agent 128 at the point in time t.sub.4 relative to their expected position from their respective trajectories, while box 136 provides a reframing of the distributions into a single combined distribution 154. The single combined distribution 154 may be considered to represent an expected position of the agent 128 given the position of the vehicle 102 on the trajectory 112).
Regarding claim 13, Lorenzetti discloses: A computer readable storage medium, storing a computer program, which, when executed by a processor, cause the processor to implement a method for controlling a vehicle (see Lorenzetti at least [col. 25, lines 46-51] In the context of software, the operations represent computer-executable instructions stored on one or more non-transitory computer-readable storage media that, when executed by one or more processors, cause a computer or autonomous vehicle to perform the recited operations and [col. 1, lines 42-45] methods, systems, and computer-readable media for controlling how a vehicle may be controlled to determine and minimize a likelihood of collision between it and other road users), comprising:
determining first ego vehicle state information of the vehicle at a current time point and first obstacle state information of an obstacle around the vehicle at the current time point (see Lorenzetti at least [col. 15, line 34] receiving a trajectory for a vehicle and [col. 15, line 41-42] receiving a trajectory for an agent);
determining, based on the first ego vehicle state information, first position probability distribution information of the vehicle at a future time point (see Lorenzetti at least [col. 3, lines 41-43] The probability distributions may be determined or defined relative to a position of the vehicle or agent along its trajectory at the time step being considered and [col. 15, lines 62-64] determining a vehicle probability distribution for the vehicle at the first time along its trajectory);
determining, based on the first obstacle state information, second position probability distribution information of the obstacle at the future time point (see Lorenzetti at least [col. 15, lines 66-67] determining an agent probability distribution for the agent at the first time along its trajectory);
determining, based on the first position probability distribution information and the second position probability distribution information, collision indication vector distribution information between the obstacle and the vehicle at the future time point (see Lorenzetti at least [col. 4, lines 17-24] to determine a probability of collision, the combined distribution may be analyzed over a relative velocity or trajectory between the agent and the vehicle in the relative positional space. The relative velocity or trajectory may comprise a vector);
determining, based on the collision indication vector distribution information, a collision risk state between the vehicle and the obstacle (see Lorenzetti at least [col. 2, lines 38-43] By determining probability of collision, control of the vehicle to minimize the risk of collision may result in more desirable behaviors, such as selecting a trajectory that includes the vehicle decelerating, coming to a stop to avoid a collision, or taking a path that avoids the agent); and
controlling a driving state of the vehicle based on the collision risk state (see Lorenzetti at least [col. 9, lines 11-13] the probability of collision of the vehicle with the agent may be determined, and so that the vehicle can therefore be controlled accordingly).
Regarding claim 14, Lorenzetti discloses: An electronic device (see Lorenzetti at least [col. 11, line 60] The vehicle computing device(s)), comprising:
a processor (see Lorenzetti at least [col. 11, lines 60-61] processor(s)); and
a memory, configured to store processor-executable instructions (see Lorenzetti at least [col. 21, lines 31-32] memory storing processor-executable instructions),
wherein the processor is configured to read the executable instructions from the memory, and execute the instructions to implement a method for controlling a vehicle (see Lorenzetti at least [col. 11, lines 65-66] processor capable of executing instructions to process data and perform operations as described herein and [col. 1, lines 42-45] methods, systems, and computer-readable media for controlling how a vehicle may be controlled to determine and minimize a likelihood of collision between it and other road users), comprising:
determining first ego vehicle state information of the vehicle at a current time point and first obstacle state information of an obstacle around the vehicle at the current time point (see Lorenzetti at least [col. 15, line 34] receiving a trajectory for a vehicle and [col. 15, line 41-42] receiving a trajectory for an agent);
determining, based on the first ego vehicle state information, first position probability distribution information of the vehicle at a future time point (see Lorenzetti at least [col. 3, lines 41-43] The probability distributions may be determined or defined relative to a position of the vehicle or agent along its trajectory at the time step being considered and [col. 15, lines 62-64] determining a vehicle probability distribution for the vehicle at the first time along its trajectory);
determining, based on the first obstacle state information, second position probability distribution information of the obstacle at the future time point (see Lorenzetti at least [col. 15, lines 66-67] determining an agent probability distribution for the agent at the first time along its trajectory);
determining, based on the first position probability distribution information and the second position probability distribution information, collision indication vector distribution information between the obstacle and the vehicle at the future time point (see Lorenzetti at least [col. 4, lines 17-24] to determine a probability of collision, the combined distribution may be analyzed over a relative velocity or trajectory between the agent and the vehicle in the relative positional space. The relative velocity or trajectory may comprise a vector);
determining, based on the collision indication vector distribution information, a collision risk state between the vehicle and the obstacle (see Lorenzetti at least [col. 2, lines 38-43] By determining probability of collision, control of the vehicle to minimize the risk of collision may result in more desirable behaviors, such as selecting a trajectory that includes the vehicle decelerating, coming to a stop to avoid a collision, or taking a path that avoids the agent); and
controlling a driving state of the vehicle based on the collision risk state (see Lorenzetti at least [col. 9, lines 11-13] the probability of collision of the vehicle with the agent may be determined, and so that the vehicle can therefore be controlled accordingly).
Regarding claim 15¸ Lorenzetti discloses: The electronic device according to claim 14, wherein determining, based on the collision indication vector distribution, the collision risk state between the vehicle and the obstacle comprises:
determining a current road scenario type based on the first ego vehicle state information and the first obstacle state information (see Lorenzetti at least [col. 16, lines 22-29] The probability distributions may be based on a state of the agent or vehicle, as defined by one or more of the vehicle systems. The state of an agent may include information relating to at least one of an entity type, a direction of travel, a trajectory, a current position, a behavior profile, whether the agent is being controlled manually or autonomously, whether the agent is static or in motion, or environmental conditions);
determining a current collision region based on the current road scenario type (see Lorenzetti at least [col. 19, lines 38-43] In order to take account of a size of the vehicle and the agent, an area that may be occupied by the vehicle and agent together may be used to evaluate the combined distribution);
determining, based on the current collision region and the collision indication vector distribution information, a collision risk value at the future time point (see Lorenzetti at least [col. 19, lines 63-64] The region may be then used to determine a probability of collision over the time step and [col. 17, lines 8-11] The probability values may be used to determine a further value, which may represent an overall probability of collision or a cost value); and
determining, based on the collision risk value and a collision risk threshold, the collision risk state between the vehicle and the obstacle (see Lorenzetti at least [col. 6, lines 13-15] A cost value may be compared to a threshold value and action may be taken if the cost value is above the threshold value).
Regarding claim 16, Lorenzetti discloses: The electronic device according to claim 15, wherein the determining, based on the current collision region and the collision indication vector distribution information, a collision risk value at the future time point comprises:
determining an integral value of collision indication vector distribution information in the current collision region, as the collision risk value corresponding to the future time point (see Lorenzetti at least [col. 19, lines 52-56] The velocity vector or trajectory may indicate a relative direction of travel, over which an area or dimension of the vehicle, agent, or combination may be integrated to determine a probability of collision over the area occupied by the vehicle and/or agent).
Regarding claim 19, Lorenzetti discloses: The electronic device according to claim 15, wherein the determining a current collision region based on the current road scenario type comprises:
determining a first length and a first width of the vehicle, and a second length and a second width of the obstacle (see Lorenzetti at least [col. 4, line 67 – col. 5, line 2] one or more dimensions of the vehicle and/or agent, such as a width of the vehicle and/or agent, may be determined); and
determining the current collision region based on the current road scenario type, the first length, the first width, the second length, and the second width (see Lorenzetti at least [col. 5, lines 2-6] The dimension or dimensions may be used to determine a plurality of points around the relative position at which the probability of collision may be determined by evaluating the distribution or a function based thereon).
Regarding claim 20, Lorenzetti discloses: The electronic device according to claim 14, wherein the determining, based on the first position probability distribution information and the second position probability distribution information, collision indication vector distribution information between the obstacle and the vehicle at the future time point comprises:
superposing first position probability distribution information and second position probability distribution information based on preset coefficients, to obtain collision indication vector distribution information corresponding to the future time point (see Lorenzetti at least [col. 8, lines 60-62] at box 134, there is some overlap between the ellipses of vehicle 102 and the agent 128 at time t.sub and [col. 8, line 67 – col. 9, line 8] box 134 shows two separate probability distributions 146, 148 representing likely positions of the vehicle 102 and agent 128 at the point in time t.sub.4 relative to their expected position from their respective trajectories, while box 136 provides a reframing of the distributions into a single combined distribution 154. The single combined distribution 154 may be considered to represent an expected position of the agent 128 given the position of the vehicle 102 on the trajectory 112).
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.
Claim(s) 4, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lorenzetti, in view of JP 7276690 B2 Shota Katayama et al. (hereinafter Katayama).
Regarding claim 4, Lorenzetti discloses: The method according to claim 2, wherein the determining a current road scenario type based on the first ego vehicle state information and the first obstacle state information comprises:
determining a lateral velocity of the obstacle based on the first obstacle state information (see Lorenzetti at least [col. 16, lines 12-16] the probability distributions may be based on a velocity of the entity for which they are determined. The velocity may be a longitudinal velocity or a lateral velocity or a combination thereof).
Lorenzetti does not teach: determining, based on the first ego vehicle state information, a turning tendency state of the vehicle; and determining the current road scenario type based on the lateral velocity of the obstacle and the turning tendency state of the vehicle.
However, Katayama teaches: determining, based on the first ego vehicle state information, a turning tendency state of the vehicle (see Katayama at least [0082] the controller 10 determines whether or not the vehicle 1 crosses the oncoming lane based on the travel route of the vehicle 1 preset in the navigation device 30 and [0042] In FIG. 2, the vehicle 1 is about to turn right at the intersection C and cross the oncoming lane 4b); and
determining the current road scenario type based on the lateral velocity of the obstacle and the turning tendency state of the vehicle (see Katayama at least [0078] Next, in step S103, the controller 10 determines whether or not the oncoming vehicle continues to travel in the oncoming lane… direction, the speed (absolute value) of the moving object is a predetermined value (for example, 25 km/h) or more, and the lateral speed of the moving object (in the direction orthogonal to the direction in which the oncoming lane extends) speed) is equal to or less than a predetermined value (for example, 3.5 m/s), it is determined that the oncoming vehicle continuously travels in the oncoming lane and [0081] Next, in step S107, the controller 10 determines whether or not the first trajectory and the second trajectory overlap... In the example of FIG. 2, the first trajectory R1 and the second trajectory R2 overlap).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the vehicle collision prediction and avoidance method disclosed by Lorenzetti to include the multiple vehicle plan determination of Katayama. One of ordinary skill in the art would have been motivated to make this modification because identifying circumstances in which ego vehicle interacts with another vehicle such that collision may occur due to turning interactions, identification of such a situation may educate a vehicle control step such as automatic braking, as suggested by Katayama (see Katayama at least [0040] when the vehicle 1 crosses the oncoming lane, the controller 10 determines whether the vehicle 1 will collide with an oncoming vehicle traveling in the oncoming lane (collision determination control), It activates an alarm device and automatically brakes to avoid a collision (collision avoidance control)).
Regarding claim 17, Lorenzetti discloses: The electronic device according to claim 15, wherein the determining a current road scenario type based on the first ego vehicle state information and the first obstacle state information comprises:
determining a lateral velocity of the obstacle based on the first obstacle state information (see Lorenzetti at least [col. 16, lines 12-16] the probability distributions may be based on a velocity of the entity for which they are determined. The velocity may be a longitudinal velocity or a lateral velocity or a combination thereof).
Lorenzetti does not teach: determining, based on the first ego vehicle state information, a turning tendency state of the vehicle; and determining the current road scenario type based on the lateral velocity of the obstacle and the turning tendency state of the vehicle.
However, ___ teaches: determining, based on the first ego vehicle state information, a turning tendency state of the vehicle (see Katayama at least [0082] the controller 10 determines whether or not the vehicle 1 crosses the oncoming lane based on the travel route of the vehicle 1 preset in the navigation device 30 and [0042] In FIG. 2, the vehicle 1 is about to turn right at the intersection C and cross the oncoming lane 4b); and
determining the current road scenario type based on the lateral velocity of the obstacle and the turning tendency state of the vehicle (see Katayama at least [0078] Next, in step S103, the controller 10 determines whether or not the oncoming vehicle continues to travel in the oncoming lane… direction, the speed (absolute value) of the moving object is a predetermined value (for example, 25 km/h) or more, and the lateral speed of the moving object (in the direction orthogonal to the direction in which the oncoming lane extends) speed) is equal to or less than a predetermined value (for example, 3.5 m/s), it is determined that the oncoming vehicle continuously travels in the oncoming lane and [0081] Next, in step S107, the controller 10 determines whether or not the first trajectory and the second trajectory overlap... In the example of FIG. 2, the first trajectory R1 and the second trajectory R2 overlap).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the vehicle collision prediction and avoidance device disclosed by Lorenzetti to include the multiple vehicle plan determination of Katayama. One of ordinary skill in the art would have been motivated to make this modification because identifying circumstances in which ego vehicle interacts with another vehicle such that collision may occur due to turning interactions, identification of such a situation may educate a vehicle control step such as automatic braking, as suggested by Katayama (see Katayama at least [0040] when the vehicle 1 crosses the oncoming lane, the controller 10 determines whether the vehicle 1 will collide with an oncoming vehicle traveling in the oncoming lane (collision determination control), It activates an alarm device and automatically brakes to avoid a collision (collision avoidance control)).
Claim(s) 5, 7, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lorenzetti, in view of US 20040193347 A1 Harumoto, Satoshi et al. (hereinafter Harumoto).
Regarding claim 5, Lorenzetti discloses: The method according to claim 2.
Lorenzetti does not teach: wherein the determining a current collision region based on the current road scenario type comprises: in response to the current road scenario type being a first type, determining an overtaking tendency state of the vehicle; determining the current collision region based on the overtaking tendency state of the vehicle; in response to the current road scenario type being a second type, determining the current collision region based on a longitudinal velocity of the obstacle; and in response to the current road scenario type being a third type, determining the current collision region based on a turning direction of the vehicle.
However, Harumoto teaches: wherein the determining a current collision region based on the current road scenario type comprises:
in response to the current road scenario type being a first type, determining an overtaking tendency state of the vehicle (see Harumoto at least [0149] at the time of passing (detected by the information of the road and the direction indicator));
determining the current collision region based on the overtaking tendency state of the vehicle (see Harumoto at least [0149] For example, at the time of passing (detected by the information of the road and the direction indicator), the danger area, the caution area, and the precaution area on the right side of the vehicle become wider (which are also changed by the influence of speed and the like), and become narrower on the left side thereof);
in response to the current road scenario type being a second type, determining the current collision region based on a longitudinal velocity of the obstacle (see Harumoto at least [0149] even when pedestrians are advancing in a certain direction at a predetermined speed, they have a possibility of taking actions, such as increasing the speed, stopping, or rushing out to the right or left. Therefore, the danger area, the caution area, and the precaution area are set within a range based on an action that the pedestrian may take); and
in response to the current road scenario type being a third type, determining the current collision region based on a turning direction of the vehicle (see Harumoto at least [0149] in the case of bicycles, the danger area, the caution area, and the precaution area in the right and left direction are set, assuming a case of turning sideways, not rushing out).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the vehicle collision prediction and avoidance method disclosed by Lorenzetti to include the different danger area setting parameters based on the conditions surrounding the ego vehicle of Harumoto. One of ordinary skill in the art would have been motivated to make this modification because the danger areas are affected by the different agents in the scene, including the own vehicle itself, as suggested by Harumoto (see Harumoto at least [0149] in the case of a traveling vehicle, it is necessary to set the danger area, the caution area, and the precaution area sufficiently wide in the advancing direction. These areas will change according to the condition of the own vehicle).
Regarding claim 7, Lorenzetti and Harumoto disclose: The method according to claim 5, wherein the determining a current collision region based on the current road scenario type comprises:
determining a first length and a first width of the vehicle, and a second length and a second width of the obstacle (see Lorenzetti at least [col. 4, line 67 – col. 5, line 2] one or more dimensions of the vehicle and/or agent, such as a width of the vehicle and/or agent, may be determined); and
determining the current collision region based on the current road scenario type, the first length, the first width, the second length, and the second width (see Lorenzetti at least [col. 5, lines 2-6] The dimension or dimensions may be used to determine a plurality of points around the relative position at which the probability of collision may be determined by evaluating the distribution or a function based thereon).
Regarding claim 18, Lorenzetti discloses: The electronic device according to claim 15.
Lorenzetti does not teach: wherein the determining a current collision region based on the current road scenario type comprises: in response to the current road scenario type being a first type, determining an overtaking tendency state of the vehicle; determining the current collision region based on the overtaking tendency state of the vehicle; in response to the current road scenario type being a second type, determining the current collision region based on a longitudinal velocity of the obstacle; and in response to the current road scenario type being a third type, determining the current collision region based on a turning direction of the vehicle.
However, Harumoto teaches: wherein the determining a current collision region based on the current road scenario type comprises:
in response to the current road scenario type being a first type, determining an overtaking tendency state of the vehicle (see Harumoto at least [0149] at the time of passing (detected by the information of the road and the direction indicator));
determining the current collision region based on the overtaking tendency state of the vehicle (see Harumoto at least [0149] For example, at the time of passing (detected by the information of the road and the direction indicator), the danger area, the caution area, and the precaution area on the right side of the vehicle become wider (which are also changed by the influence of speed and the like), and become narrower on the left side thereof);
in response to the current road scenario type being a second type, determining the current collision region based on a longitudinal velocity of the obstacle (see Harumoto at least [0149] even when pedestrians are advancing in a certain direction at a predetermined speed, they have a possibility of taking actions, such as increasing the speed, stopping, or rushing out to the right or left. Therefore, the danger area, the caution area, and the precaution area are set within a range based on an action that the pedestrian may take); and
in response to the current road scenario type being a third type, determining the current collision region based on a turning direction of the vehicle (see Harumoto at least [0149] in the case of bicycles, the danger area, the caution area, and the precaution area in the right and left direction are set, assuming a case of turning sideways, not rushing out).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the vehicle collision prediction and avoidance device disclosed by Lorenzetti to include the different danger area setting parameters based on the conditions surrounding the ego vehicle of Harumoto. One of ordinary skill in the art would have been motivated to make this modification because the danger areas are affected by the different agents in the scene, including the own vehicle itself, as suggested by Harumoto (see Harumoto at least [0149] in the case of a traveling vehicle, it is necessary to set the danger area, the caution area, and the precaution area sufficiently wide in the advancing direction. These areas will change according to the condition of the own vehicle).
Claim(s) 9, 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lorenzetti, in view of US 20240300527 A1 LU; Jack et al. (hereinafter Lu).
Regarding claim 9, Lorenzetti discloses: The method according to claim 1.
Lorenzetti does not teach: wherein the determining, based on the first ego vehicle state information, first position probability distribution information of the vehicle at a future time point comprises: determining a first state mean based on the first ego vehicle state information; determining a plurality of sampling states corresponding to the vehicle based on the first state mean and a first state variance pre-obtained; determining, based on a vehicle state transition rule pre-obtained and the plurality of sampling states corresponding to the vehicle, first state probability distribution information of the vehicle corresponding to the future time point; and determining, based on the first state probability distribution information, the first position probability distribution information.
However, Lu teaches: wherein the determining, based on the first ego vehicle state information, first position probability distribution information of the vehicle at a future time point comprises:
determining a first state mean based on the first ego vehicle state information (see Lu at least [0062] because the distribution function is a Gaussian distribution, the sampling may be from a normal Gaussian distribution (with mean=0 and covariance matrix=identity));
determining a plurality of sampling states corresponding to the vehicle based on the first state mean and a first state variance pre-obtained (see Lu at least [0062] Then the sample is scales and shifted the sample according to the predicted mean and the value of the variance schedule at that diffusion timestep);
determining, based on a vehicle state transition rule pre-obtained and the plurality of sampling states corresponding to the vehicle, first state probability distribution information of the vehicle corresponding to the future time point (see Lu at least [0062] The agent state sampler (310) is configured to sample the distribution function to obtain a revised set of agent states. Virtually any implementation of random sampling may be used. For example, the agent state sampler may use inverse transform sampling); and
determining, based on the first state probability distribution information, the first position probability distribution information (see Lu at least [0121] The initial set of agent vectors are randomly generated to have random positions, headings and sizes of the agents in the geographic region. Over the period of T diffusion timesteps as demonstrated in Blocks 606 to 608, the process refines the agent vectors so that agents are placed in similar positions to how the agents may be in the real-world).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the vehicle collision prediction and avoidance method disclosed by Lorenzetti to include the agent state distribution sampling and processing of Lu. One of ordinary skill in the art would have been motivated to make this modification because the prediction-based nature of autonomous vehicle environments requires such advanced simulated agent predictions, as suggested by Lu (see Lu at least [0022] Embodiments of the invention may be used as part of generating a simulated environment for the training and testing of autonomous systems… Examples of autonomous systems include self-driving vehicles).
Regarding claim 10, Lorenzetti discloses: The method according to claim 1.
Lorenzetti does not teach: wherein the determining, based on the first obstacle state information, second position probability distribution information of the obstacle at the future time point comprises:
determining a second state mean based on the first obstacle state information; determining a plurality of sampling states corresponding to the obstacle based on the second state mean and a second state variance pre-obtained; determining, based on an obstacle state transition rule pre-obtained and the plurality of sampling states corresponding to the obstacle, second state probability distribution information of the obstacle at the future time point; and determining, based on the second state probability distribution information, the second position probability distribution information of the obstacle.
However, Lu teaches: wherein the determining, based on the first obstacle state information, second position probability distribution information of the obstacle at the future time point comprises:
determining a second state mean based on the first obstacle state information (see Lu at least [0062] because the distribution function is a Gaussian distribution, the sampling may be from a normal Gaussian distribution (with mean=0 and covariance matrix=identity) and [0025] the simulated environment (104) includes a simulation of the objects (i.e., simulated objects or assets) and background in the real world, including the natural objects, construction, buildings and roads, obstacles, as well as other autonomous and non-autonomous objects);
determining a plurality of sampling states corresponding to the obstacle based on the second state mean and a second state variance pre-obtained (see Lu at least [0062] Then the sample is scales and shifted the sample according to the predicted mean and the value of the variance schedule at that diffusion timestep);
determining, based on an obstacle state transition rule pre-obtained and the plurality of sampling states corresponding to the obstacle, second state probability distribution information of the obstacle at the future time point (see Lu at least [0062] The agent state sampler (310) is configured to sample the distribution function to obtain a revised set of agent states. Virtually any implementation of random sampling may be used. For example, the agent state sampler may use inverse transform sampling); and
determining, based on the second state probability distribution information, the second position probability distribution information of the obstacle (see Lu at least [0121] The initial set of agent vectors are randomly generated to have random positions, headings and sizes of the agents in the geographic region. Over the period of T diffusion timesteps as demonstrated in Blocks 606 to 608, the process refines the agent vectors so that agents are placed in similar positions to how the agents may be in the real-world).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the vehicle collision prediction and avoidance device disclosed by Lorenzetti to include the agent state distribution sampling and processing of Lu. One of ordinary skill in the art would have been motivated to make this modification because the prediction-based nature of autonomous vehicle environments requires such advanced simulated agent predictions, as suggested by Lu (see Lu at least [0022] Embodiments of the invention may be used as part of generating a simulated environment for the training and testing of autonomous systems… Examples of autonomous systems include self-driving vehicles).
Claim(s) 11, 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lorenzetti, in view of US 20060074496 A1 Taware; Avinash Vinayak et al. (hereinafter Taware).
Regarding claim 11, Lorenzetti discloses: The method according to claim 1, wherein the determining the first obstacle state information of the obstacle around the vehicle at the current time point comprises:
determining second obstacle state information of the obstacle at the current time point perceived respectively by multiple types of sensors (see Lorenzetti at least [col. 7, lines 44-46] The agent trajectory 130 may be determined based on sensor data gathered by the one or more sensors 116 and relating to the agent 128).
Lorenzetti does not teach: weighting the second obstacle state information based on a credibility weight corresponding to a sensor of respective types pre-obtained, to obtain the first obstacle state information of the obstacle.
However, Taware teaches: weighting the second obstacle state information based on a credibility weight corresponding to a sensor of respective types pre-obtained, to obtain the first obstacle state information of the obstacle (see Taware at least [0031] The fusion of the sensor signals is achieved by establishing a weighted average of the sensor signals, the sensor signals being weighted by their corresponding confidence values).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the vehicle collision prediction and avoidance method disclosed by Lorenzetti to include the credibility-weighted sensor fusion model of Taware. One of ordinary skill in the art would have been motivated to make this modification because information being collected from different sensors may be retained or valued based on the reliability of the sensor, as suggested by Taware (see Taware at least [0033] accepting or rejecting a sensor signal based on its confidence value and disengaging a sensor signal from sensor fusion when the sensor signal is below a threshold confidence value).
Regarding claim 12, Lorenzetti and Taware disclose: The method according to claim 11, wherein the credibility weight corresponding to the sensor of the respective types is obtained by:
determining an obstacle state ground truth of a preset obstacle and an obstacle state predicted value of the preset obstacle perceived by the sensor of the respective types (see Taware at least [0031] The method also comprises comparing each of the sensor signal values to a corresponding expected sensor signal value, as represented by block 14); and
determining the credibility weight corresponding to the sensor of the respective types based on the obstacle state ground truth and the obstacle state predicted value corresponding to the sensor of the respective types (see Taware at least [0031] the method comprises establishing a corresponding confidence value for each sensor signal value based on the comparison of the sensor signal value to the corresponding expected sensor signal value, as represented by block 16. If the deviation of the sensor signal value from the expected sensor signal value is large, a low confidence value is assigned to the sensor signal. If the deviation of the sensor signal value from the expected sensor signal value is small, a high confidence value is assigned to the sensor signal).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the vehicle collision prediction and avoidance method disclosed by Lorenzetti to include the credibility-weighted sensor fusion model based on comparison between actual and expected values of Taware. One of ordinary skill in the art would have been motivated to make this modification because information being collected from different sensors may be retained or valued based on the reliability of the sensor, as suggested by Taware (see Taware at least [0033] accepting or rejecting a sensor signal based on its confidence value and disengaging a sensor signal from sensor fusion when the sensor signal is below a threshold confidence value).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
CN 115503756 A HAO, Dong-hao et al. discloses: The application claims an intelligent driving decision method, decision device and vehicle, it can be applied to intelligent driving field, comprising: identifying intention is not determined object (i.e., game object) and its own n road topology, and determining the game object along each road topology driving intention probability (totally n), constructing a sampling game space of m game strategy, calculating the strategy cost of m game strategy on each road topology (common n group), determining n target strategy cost satisfy the preset condition, and determining the self-decision result according to the target game strategy (one of m). under the condition that the object is not determined by the object application using the observed information reasoning intention to unclear the intention probability of the object, based on the uncertainty interaction game decision, to solve the best action of the vehicle, so that the bicycle can rationally respond to the jumping of the dynamic object intention, reducing the unreasonable point brake and error-snatching row, improving riding comfort of bicycle, safety and passing ability.
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/ELLE ROSE KNUDSON/Examiner, Art Unit 3667
/Hitesh Patel/Supervisory Patent Examiner, Art Unit 3667
6/29/26
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