Office Action 3: Notice of Allowance (2025-04-18)

CLAIMS:
Listing of Claims:

AMENDMENT TO THE CLAIMSThis listing of claims will replace all prior versions, and listings, of claims in the application.

1\. (Currently Amended) An apparatus for preventing car collision, the apparatus comprising:
a communication unit configured to communicate with a preceding vehicle or a camera;
and a processor operatively connected to the communication unit,wherein the processor is configured to:
obtain information about a third vehicle in front of the preceding vehicle acquired by the preceding vehicle;
calculate a deceleration amount for preventing a collision with the third vehicle based on the information about the third vehicle;
predict a preset event related to the preceding vehicle based on a change in a position of the preceding vehicle; and
control an autonomous driving vehicle based on the deceleration amount according to a prediction result regarding the preset event,
wherein the processor detects the change in the position of the preceding vehicle and predicts occurrence of the preset event using an event prediction model based on the detected change in the position of the preceding vehicle, wherein the event prediction model is trained based on information related to the detected change in the position of the preceding vehicle using a machine learning algorithm, wherein the event prediction model is trained from a first point in time to a second point in time,
wherein the first point in time is a point in time at which the preceding vehicle starts to move from a same lane as a lane in which the autonomous driving vehicle is traveling to an adjacent lane,
wherein the second point in time is a point in time at which a portion of the preceding vehicle is located at a point in the adjacent lane, the point in the adjacent lane being separated by

a predetermined distance from any one lane marking of the lane in which the preceding vehicle is traveling, andwherein the preset event related to the preceding vehicle is predicted to occur at the second point in time.

2\. (Previously Presented) The apparatus of claim 1, wherein, when it is not possible to obtain, from the preceding vehicle, the information about the third vehicle in front of the preceding vehicle, the processor obtains the information about the third vehicle in front of the preceding vehicle detected by one or more cameras installed on a road on which the autonomous driving vehicle is traveling.

3\. (Previously Presented) The apparatus of claim 2, wherein the information about the third vehicle in front of the preceding vehicle includes at least one piece of information among the group of information about a distance between the preceding vehicle and the third vehicle in front of the preceding vehicle and information about a lane in which the third vehicle in front of the preceding vehicle is located.

4\. (Cancelled)

5\. (Previously Presented) The apparatus of claim 1, wherein the preset event includes an event where one side of the preceding vehicle is positioned at a point in an adjacent lane of either side of a lane in which the preceding vehicle is traveling, the point being separated by a distance exceeding 1/3 of a total width of the preceding vehicle from any one lane marking of a corresponding side of the lane in which the preceding vehicle is traveling.

6\. (Previously Presented) The apparatus of claim 1, wherein the information about the third vehicle in front of the preceding vehicle includes at least one piece of information among the group of information about a distance between the preceding vehicle and the third vehicle in front of the preceding vehicle and information about a lane in which the third vehicle in front of the preceding vehicle is located.

7\. (Cancelled)

8\. (Currently Amended) A method for preventing car collision, the method comprising:
obtaining information about a third vehicle in front of a preceding vehicle acquired by the preceding vehicle;
calculating a deceleration amount for preventing a collision with the third vehicle based on the obtained information about the third vehicle;
predicting a preset event related to the preceding vehicle based on a change in a position of the preceding vehicle; and
controlling an autonomous driving vehicle based on the deceleration amount according to a prediction result regarding the preset event, wherein the predicting of the preset event related to the preceding vehicle comprises:
detecting the change in the position of the preceding vehicle;
training an event prediction model based on information related to the detected change in the position of the preceding vehicle using a machine learning algorithm; and
predicting occurrence of the preset event using the event prediction model based on the change in the position of the preceding vehicle, wherein the training of the event prediction model comprises training the event prediction model from a first point in time to a second point in time,
wherein the first point in time is a point in time at which the preceding vehicle starts to move from a same lane as a lane in which the autonomous driving vehicle is traveling to an adjacent lane,
wherein the second point in time is a point in time at which a portion of the preceding vehicle is located at a point in the adjacent lane, the point in the adjacent lane being separated by a predetermined distance from any one lane marking of the lane in which the preceding vehicle is traveling, and
wherein the preset event related to the preceding vehicle is predicted to occur at the second point in time.

9\. (Previously Presented) The method of claim 8, further comprising obtaining the information about the third vehicle in front of the preceding vehicle detected by one or more

cameras installed on a road on which the autonomous driving vehicle is traveling when it is not possible to obtain, from the preceding vehicle, the information about the third vehicle in front of the preceding vehicle.

10\. (Previously Presented) The method of claim 9, wherein the information about the third vehicle in front of the preceding vehicle includes at least one piece of information among the group of information about a distance between the preceding vehicle and the third vehicle in front of the preceding vehicle and information about a lane in which the third vehicle in front of the preceding vehicle is located.

11\. (Cancelled)

12\. (Previously Presented) The method of claim 8, wherein the preset event includes an event where one side of the preceding vehicle is positioned at a point in an adjacent lane of either side of a lane in which the preceding vehicle is traveling, the point being separated by a distance exceeding 1/3 of a total width of the preceding vehicle from any one lane marking of a corresponding side of the lane in which the preceding vehicle is traveling.

13\. (Previously Presented) The method of claim 8, wherein the information about the third vehicle in front of the preceding vehicle includes at least one piece of information among the group of information about a distance between the preceding vehicle and the third vehicle in front of the preceding vehicle and information about a lane in which the third vehicle in front of the preceding vehicle is located.

14\. (Cancelled)

15\. (Previously Presented) The apparatus of claim 1, wherein the third vehicle includes a vehicle that is stopped in front of the preceding vehicle.

16\. (Previously Presented) The apparatus of claim 1, wherein the processor performs deceleration control of the autonomous driving vehicle based on the deceleration amount, at a

point in time at which the processor determines that the preset event will occur based on the prediction result of the event prediction model, before a point in time at which the preset event actually occurs.

17\. (Cancelled)

18\. (Previously Presented) The apparatus of claim 1, wherein the processor predicts that the preset event will occur based on data regarding a probability of the occurrence of the preset event generated by the trained event prediction model.

19\. (Previously Presented) The method of claim 8, wherein the third vehicle includes a vehicle that is stopped in front of the preceding vehicle.

20\. (Previously Presented) The method of claim 8, wherein the controlling of the autonomous driving vehicle comprises performing deceleration control of the autonomous driving vehicle based on the deceleration amount, at a point in time at which the processor determines that the preset event will occur based on the prediction result of the event prediction model, before a point in time at which the preset event actually occurs.

21\. (Cancelled)

22\. (Currently Amended) The method of claim8,wherein the predicting of the preset event related to the preceding vehicle further comprises predicting that the preset event will occur based on data regarding a probability of the occurrence of the preset event generated by the trained event prediction model.

OFFICE ACTION:
DETAILED ACTION
Status of Claims
This Office action is in response to the after-final response filed on 04/04/2025. Claims 4, 7, 11, and 14 were previously canceled. With the filed amendment, claims 17 and 21 are also canceled. Claims 1-3, 5-6, 8-10, 12-13, 15-16, 18-20, and 22 are allowed.
Response to Amendment/Arguments
The amendment filed 04/04/2025 has been entered and applicant’s arguments filed 04/04/2025 have been fully considered. Applicant has argued that the claims are not taught by the prior art because they have been amended to include the features of claims 17 and 21, which were previously indicated as having allowable subject matter. The examiner agrees and has withdrawn the claim rejections under 35 U.S.C. § 103 accordingly.
Reasons for Allowance
The following is the examiner’s statement of reasons for allowance:
The closest prior art of record is Park (US 2022/0332319 A1) in view of Shalev-Shwartz et al. (US 2021/0078570 A1), hereinafter referred to as Shalev, and further in view of Silva et al. (US 2021/0370921 A1), hereinafter referred to as Silva.
Regarding claim 1:
Park discloses the following limitations:
“An apparatus for preventing car collision, the apparatus comprising: a communication unit configured to communicate with a preceding vehicle or a camera.” (Park ¶ 28 discloses “a vehicle including: a communicator configured to communicate with a plurality of other vehicle.” As shown in Park FIG. 7B, the plurality of vehicles can include a second vehicle 2 that travels in front of the ego vehicle 1. This at least teaches a communication unit configured to communicate with a preceding vehicle as claimed.)
“and a processor operatively connected to the communication unit.” (Park ¶¶ 150 and 219: “the cruise control device may correspond to a second controller, and may communicate with … a communicator 250,” where “The second controller 280 may be implemented as a single processor.”)
“wherein the processor is configured to: obtain information about a third vehicle in front of the preceding vehicle acquired by the preceding vehicle.” (Park FIG. 7B shows a third vehicle 3 in front of the second vehicle 2. Further, Park ¶ 274 discloses that “The vehicle may also determine whether the third vehicle is in a stationary state from the travel information and position information of the third vehicle received by the communicator 250.”)
“calculate a deceleration amount for preventing a collision with the third vehicle based on the information about the third vehicle.” (Park ¶ 275: “As shown in FIG. 7B, the vehicle may determine whether there is an obstacle that is stopped in the travelling path, and upon determining that there is an obstacle that is stopped in the path, acquire a target deceleration based on position information of the obstacle in the stationary state and the travelling velocity information of the first vehicle, and control deceleration and braking based on the acquired target deceleration.”)
“predict a preset event related to the preceding vehicle based on a change in a position of the preceding vehicle.” (Park ¶¶ 262-263: “as shown in FIG. 7A, the vehicle may, upon determining that the second vehicle 2 is not detected through the obstacle detector 220, determine that the path of the second vehicle 2 is suddenly changed (cut out). The sudden path change may include a state in which the distance to the obstacle acquired based on obstacle information detected by the obstacle detector changes to a degree greater than or equal to a reference distance within a predetermined time.”)
“and control an autonomous driving vehicle based on the deceleration amount according to a prediction result regarding the preset event.” (Park ¶¶ 267-268: “the vehicle decelerates and performs braking while maintaining the cruise control mode based on the travel information of the third vehicle in front of the vehicle during execution of the cruise control mode, so that the cruise control mode is prevented from being deactivated, thereby eliminating a hassle of the driver in resetting the cruise control mode. As described above, even when the first vehicle 1 controls acceleration because the second vehicle 2 travelling in front of the first vehicle departs from the front of the first vehicle 1, the first vehicle 1 obtains the amount of acceleration in consideration of the travel information of the third vehicle 3 travelling in front of the second vehicle 2, so that the first vehicle 1 is prevented from colliding with the third vehicle 3.”)
The following limitations are not specifically disclosed by Park, but are taught by Shalev:
“wherein the processor detects the change in the position of the preceding vehicle and predicts occurrence of the preset event using an event prediction model based on the detected change in the position of the preceding vehicle.” (Shalev ¶ 189: “If the distance between the leading vehicle's instantaneous position and the look-ahead point varies during the specific period of time, and the cumulative sum of variation exceeds a predetermined threshold (for example, 0.3 to 0.4 meters on a straight road, 0.7 to 0.8 meters on a moderately curvy road, and 1.3 to 1.7 meters on a road with sharp curves), processing unit 110 may determine that the leading vehicle is likely changing lanes.” The leading vehicle changing lanes reads on the “preset event” as claimed.)
“wherein the event prediction model is trained based on information related to the detected change in the position of the preceding vehicle using a machine learning algorithm.” (Shalev ¶ 190: “At step 584, processing unit 110 may determine whether or not leading vehicle 200 is changing lanes based on the analysis performed at step 582. … the analysis may make use of trained system (e.g., a machine learning or deep learning system), which may, for example, estimate a future path ahead of a current location of a vehicle based on an image captured at the current location.”)
to define the preset event based on “the point in the adjacent lane being separated by a predetermined distance from any one lane marking of the lane in which the preceding vehicle is traveling.” (Shalev ¶ 188: “If the difference in curvature of the snail trail and the expected curvature of the road segment exceeds a predetermined threshold, processing unit 110 may determine that the leading vehicle is likely changing lanes.”)
The combination of Park and Shalev does not specifically teach “wherein the event prediction model is trained from a first point in time to a second point in time,” where the first and second points in time occur while the autonomous driving vehicle is traveling. However, Silva does teach this limitation. (Silva ¶ 113: “the vehicle safety system 534 may train and execute machine-learned models based on observed vehicle behaviors and/or movement patterns while driving or performing particular maneuvers (e.g., backing, parking, merging, etc.).”)
None of the references in the prior art of record taken together or in combination disclose “wherein the first point in time is a point in time at which the preceding vehicle starts to move from a same lane as a lane in which the autonomous driving vehicle is traveling to an adjacent lane, wherein the second point in time is a point in time at which a portion of the preceding vehicle is located at a point in the adjacent lane … and wherein the preset event related to the preceding vehicle is predicted to occur at the second point in time” as required by claim 1.
Regarding claim 8:
Claim 8 is allowed using the same rationale applied to claim 1 above, mutatis mutandis.
Regarding claims 2-3, 5-6, 9-10, 12-13, 15-16, 18-20, and 22:
Claims 2-3, 5-6, 9-10, 12-13, 15-16, 18-20, and 22 are allowed due to their dependency upon allowed claims 1 and 8 above.
Since none of the prior art references taken separately or in combination disclose the remaining elements and features of the claimed invention in combination with the other elements and features of the claimed invention, applicant’s invention overcomes the prior art of record.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Haggblade et al. (US 2021/0269065 A1) ¶ 97 discloses “receiving log data associated with vehicles in an environment; detecting an event in the log data, wherein an event corresponds to a cut-in maneuver performed by a vehicle; determining a period of time associated with the event; accessing a portion of the log data that corresponds to the period of time; generating training data based at least in part on converting the portion of the log data that corresponds to the period of time into a top-down representation of the environment; inputting the training data into a model.”

APPLICANT REBUTTAL: REMARKSIntroduction Upon entry, claims 1-3, 5, 6, 8-10, 12, 13, 15, 16, 18-20 and 22 are pending in this application, of which claims 1 and 8 are independent. By this response, claims 1, 8, and 22 are amended, and claims 17 and 21 have been cancelled without prejudice. Claims 4, 7, 11, and 14 were previously cancelled. This Amendment does not generate new matter because the amendments to claims 1 and 8 incorporate subject matter from cancelled claims 17 and 21.The arguments and contentions presented herein should not be construed as any acquiescence or agreement by Applicant with the reasoning stated in the Office Action regardless of whether or not the following remarks specifically address any particular argument or contention from the Office Action. Furthermore, although certain distinctions between the claims of the present application and the cited references are addressed below, these distinctions are not necessarily exhaustive.Reconsideration of this application for allowance of all pending claims is hereby respectfully requested in view of the amendments to the claims and following remarks.Allowable Subject Matter Applicant acknowledges with appreciation that claims 17 and 21 are indicated as allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.Claim Reiections Under 35 U.S.C. § 103 Claims 1, 8, 15 and 19 have been rejected under 35 U.S.C. § 103 as being unpatentable over Park (U.S. Publication No. 2022/0332319, hereinafter "Park") in view of Shalev-Shwartz et al. (U.S. Publication No. 2021/0078570, hereinafter "Shalev-Shwartz").Claims 2, 3, 6, 9, 10 and 13 have been rejected under 35 U.S.C. § 103 as being unpatentable over Park in view of Shalev-Shwartz as applied to claims 1 and 8 above, and further in view of Grace et al. (U.S. Publication No. 2021/0304607, hereinafter "Grace").Claims 5 and 12 have been rejected under 35 U.S.C. § 103 as being unpatentable over Park in view of Shalev-Shwartz as applied to claims 1 and 8 above, and further in view of Kato (U.S. Publication No. 2020/0247417, hereinafter "Kato").Claims 16 and 20 have been rejected under 35 U.S.C. § 103 as being unpatentable over Park in view of Shalev as applied to claims 1 and 20 above, and further in view of Schmudderich et al. (U.S. Publication No. 2013/0054106, hereinafter "Schmudderich").Claims 18 and 22 have been rejected under 35 U.S.C. § 103 as being unpatentable over Park in view of Shalev as applied to claims 1 and 8 above, and further in view of Xue et al. (U.S.Publication No. 2022/0375349, hereinafter "Xue").Independent claim 1 is amended to include the features "the event prediction model is trained from a first point in time to a second point in time, ... the first point in time is a point in time at which the preceding vehicle starts to move from a same lane as a lane in which the autonomous driving vehicle is traveling to an adjacent lane, ... the second point in time is a point in time at which a portion of the preceding vehicle is located at a point in the adjacent lane, the point in the adjacent lane being separated by a predetermined distance from any one lane marking of the lane in which the preceding vehicle is traveling, and ... the preset event related to the preceding vehicle is predicted to occur at the second point in time," which Applicant believes is substantially similar to subject matter of claim 17 (which is indicated as allowable by the Examiner).Independent claim 8 is amended to include the features "the training of the event prediction model comprises training the event prediction model from a first point in time to a second point in time, ... the first point in time is a point in time at which the preceding vehicle starts to move from a same lane as a lane in which the autonomous driving vehicle is traveling to an adjacent lane, ... the second point in time is a point in time at which a portion of the preceding vehicle is located at a point in the adjacent lane, the point in the adjacent lane being separated by a predetermined distance from any one lane marking of the lane in which the preceding vehicle is traveling, and ... the preset event related to the preceding vehicle is predicted to occur at the second point in time," which Applicant believes is substantially similar to subject matter of claim 21 (which is indicated as allowable by the Examiner).In view of the foregoing amendments to claims 1 and 8, it is respectfully submitted that none of the cited art, whether taken alone or in combination, disclose, teach or suggest the aforementioned features of claims 1 and 8.Applicant therefore submits that claims 1 and 8 and the claims dependent thereupon are in condition for allowance, and the present application should pass to issue.Conclusion In view of the above amendments and remarks, Applicant submits that this application should be allowed and the case passed to issue. If there are any questions regarding this Amendment or the application in general, a telephone call to the undersigned would be appreciated to expedite the prosecution of the application.Applicant believes no fee is due with this response. However, if a fee is due, please charge our Deposit Account No. 50-0310 from which the undersigned is authorized to draw.Respectfully submitted, Dated: April 4, 2025


CITED REFERENCE SUMMARIES: Ref US20210370921A1: Claims:
A system comprising: one or more processors; and one or more non-transitory computer-readable media storing computer-executable instructions that, when executed, cause the one or more processors to perform operations comprising: receiving state data associated with an object in an environment, the state data based at least in part on sensor data captured by a sensor of a vehicle, and the state data associated with a first time; determining, based at least in part on the state data and a perturbation parameter, a plurality of perturbed trajectories associated with the object; receiving a trajectory associated with the vehicle in the environment; determining, based at least in part on a first perturbed trajectory and the trajectory associated with the vehicle, an intersection associated with the object and the vehicle at a second time after the first time; determining, based at least in part on the intersection, an action for the vehicle; and controlling operation of the vehicle based at least in part on the action.
one or more processors; and
one or more non-transitory computer-readable media storing computer-executable instructions that, when executed, cause the one or more processors to perform operations comprising: receiving state data associated with an object in an environment, the state data based at least in part on sensor data captured by a sensor of a vehicle, and the state data associated with a first time; determining, based at least in part on the state data and a perturbation parameter, a plurality of perturbed trajectories associated with the object; receiving a trajectory associated with the vehicle in the environment; determining, based at least in part on a first perturbed trajectory and the trajectory associated with the vehicle, an intersection associated with the object and the vehicle at a second time after the first time; determining, based at least in part on the intersection, an action for the vehicle; and controlling operation of the vehicle based at least in part on the action.
receiving state data associated with an object in an environment, the state data based at least in part on sensor data captured by a sensor of a vehicle, and the state data associated with a first time;
determining, based at least in part on the state data and a perturbation parameter, a plurality of perturbed trajectories associated with the object;
receiving a trajectory associated with the vehicle in the environment;
determining, based at least in part on a first perturbed trajectory and the trajectory associated with the vehicle, an intersection associated with the object and the vehicle at a second time after the first time;
determining, based at least in part on the intersection, an action for the vehicle; and
controlling operation of the vehicle based at least in part on the action.
The system of claim 1 , the operations further comprising: determining, based at least in part on the plurality of perturbed trajectories and the trajectory associated with the vehicle, a number of intersections associated with the plurality of perturbed trajectories; determining that the number of intersections meets or exceeds a threshold number; and determining, further based at least in part on the number of intersections, the action for the vehicle.
determining, based at least in part on the plurality of perturbed trajectories and the trajectory associated with the vehicle, a number of intersections associated with the plurality of perturbed trajectories;
determining that the number of intersections meets or exceeds a threshold number; and
determining, further based at least in part on the number of intersections, the action for the vehicle.
The system of claim 2 , the operations further comprising: determining a probability associated with the first perturbed trajectory, the probability indicative of a likelihood for the object to follow the first perturbed trajectory; and determining, further based at least in part on the probability and the number of intersections, the action for the vehicle.
determining a probability associated with the first perturbed trajectory, the probability indicative of a likelihood for the object to follow the first perturbed trajectory; and
determining, further based at least in part on the probability and the number of intersections, the action for the vehicle.
The system of claim 1 , wherein the state data comprises acceleration data associated with the object, a steering angle rate associated with the object, and a classification associated with the object, wherein determining the first perturbed trajectory comprises varying at least one of the acceleration data or the steering angle rate, based at least in part on the perturbation parameter, and wherein the perturbation parameter is based at least in part on the classification associated with the object.
wherein determining the first perturbed trajectory comprises varying at least one of the acceleration data or the steering angle rate, based at least in part on the perturbation parameter, and
wherein the perturbation parameter is based at least in part on the classification associated with the object.
The system of claim 1 , wherein determining the intersection comprises: determining a first region associated with the vehicle at the second time, based at least in part on the trajectory; determining a second region associated with the object at the second time, based at least in part on the first perturbed trajectory; and determining an overlap between the first region and the second region.
determining a first region associated with the vehicle at the second time, based at least in part on the trajectory;
determining a second region associated with the object at the second time, based at least in part on the first perturbed trajectory; and
determining an overlap between the first region and the second region.
A method comprising: receiving state data associated with an object in an environment; receiving a perceived trajectory associated with the object; determining, based at least in part on the perceived trajectory, a perturbed trajectory; receiving a trajectory associated with a vehicle in the environment; determining, based at least in part on the perturbed trajectory and the trajectory, a likelihood of collision between the object and the vehicle; and determining, based at least in part on the likelihood of collision, an action for the vehicle.
receiving state data associated with an object in an environment;
receiving a perceived trajectory associated with the object;
determining, based at least in part on the perceived trajectory, a perturbed trajectory;
receiving a trajectory associated with a vehicle in the environment;
determining, based at least in part on the perturbed trajectory and the trajectory, a likelihood of collision between the object and the vehicle; and
determining, based at least in part on the likelihood of collision, an action for the vehicle.
The method of claim 6 , wherein determining the perturbed trajectory comprises determining a plurality of perturbed trajectories, the method further comprising: determining, based at least in part on the plurality of perturbed trajectories and the trajectory, a number of potential collisions between the vehicle and the object; determining that the number of potential collisions meets or exceeds a threshold number; and determining, further based at least in part on the number of potential collisions, the action for the vehicle.
determining, based at least in part on the plurality of perturbed trajectories and the trajectory, a number of potential collisions between the vehicle and the object;
determining that the number of potential collisions meets or exceeds a threshold number; and
determining, further based at least in part on the number of potential collisions, the action for the vehicle.
The method of claim 7 , further comprising determining a probability of a collision between the vehicle and the object, based at least in part on the number of potential collisions relative to a number of the plurality of perturbed trajectories.
The method of claim 7 , wherein the state data comprises acceleration data associated with the object, a steering angle rate associated with the object, and wherein determining the plurality of perturbed trajectories associated with the object comprises varying at least one of the acceleration data or the steering angle rate.
wherein determining the plurality of perturbed trajectories associated with the object comprises varying at least one of the acceleration data or the steering angle rate.
The method of claim 9 , wherein the state data further comprises a classification associated with the object, and wherein determining the perturbed trajectory is based at least in part on the classification associated with the object.
wherein determining the perturbed trajectory is based at least in part on the classification associated with the object.
The method of claim 6 , further comprising: determining a probability associated with the perturbed trajectory; altering, based at least in part on the probability, the likelihood of collision; and determining, further based at least in part on the probability, the action for the vehicle.
determining a probability associated with the perturbed trajectory;
altering, based at least in part on the probability, the likelihood of collision; and
determining, further based at least in part on the probability, the action for the vehicle.
The method of claim 6 , wherein determining the intersection comprises: determining a first bounding box associated with the vehicle; determining a second bounding box associated with the object; determining a first position of the first bounding box at the second time, based at least in part on the trajectory; determining a second position of the second bounding box at the second time, based at least in part on the perturbed trajectory; and determining an overlap between the first bounding box and the second bounding box at the second time, based at least in part on the first position and the second position.
determining a first bounding box associated with the vehicle;
determining a second bounding box associated with the object;
determining a first position of the first bounding box at the second time, based at least in part on the trajectory;
determining a second position of the second bounding box at the second time, based at least in part on the perturbed trajectory; and
determining an overlap between the first bounding box and the second bounding box at the second time, based at least in part on the first position and the second position.
The method of claim 6 , wherein the state data is based at least in part on sensor data captured by a sensor of the vehicle, and wherein the state data comprises acceleration data associated with the object and a steering angle rate associated with the object, and wherein determining the perturbed trajectory comprises varying one or more of the acceleration data or the steering angle rate, based at least in part on a perturbation parameter.
wherein determining the perturbed trajectory comprises varying one or more of the acceleration data or the steering angle rate, based at least in part on a perturbation parameter.
One or more non-transitory computer-readable media storing instructions executable by a processor, wherein the instructions, when executed, cause the processor to perform operations comprising: receiving state data associated with an object in an environment; receiving a perceived trajectory associated with the object; determining, based at least in part on the perceived trajectory, a perturbed trajectory; receiving a trajectory associated with a vehicle in the environment; determining, based at least in part on the perturbed trajectory and the trajectory, a likelihood of collision between the object and the vehicle; and determining, based at least in part on the likelihood of collision, an action for the vehicle.
The one or more non-transitory computer-readable media of claim 14 , wherein determining the perturbed trajectory comprises determining a plurality of perturbed trajectories, the operations further comprising: determining, based at least in part on the plurality of perturbed trajectories and the trajectory, a number of potential collisions between the vehicle and the object; determining that the number of potential collisions meets or exceeds a threshold number; and determining, further based at least in part on the number of potential collisions, the action for the vehicle.
The one or more non-transitory computer-readable media of claim 15 , the operations further comprising: determining a probability of a collision between the vehicle and the object, based at least in part on the number of potential collisions relative to a number of the plurality of perturbed trajectories.
determining a probability of a collision between the vehicle and the object, based at least in part on the number of potential collisions relative to a number of the plurality of perturbed trajectories.
The one or more non-transitory computer-readable media of claim 15 , wherein the state data comprises acceleration data associated with the object, a steering angle rate associated with the object, and wherein determining the plurality of perturbed trajectories associated with the object comprises varying at least one of the acceleration data or the steering angle rate.
The one or more non-transitory computer-readable media of claim 17 , wherein the state data further comprises a classification associated with the object, and wherein determining the perturbed trajectory is based at least in part on the classification associated with the object.
The one or more non-transitory computer-readable media of claim 14 , the operations further comprising: determining a probability associated with the perturbed trajectory; altering, based at least in part on the probability, the likelihood of collision; and determining, further based at least in part on the probability, the action for the vehicle.
The one or more non-transitory computer-readable media of claim 14 , wherein determining the likelihood of collision between the object and the vehicle comprises: determining a first region associated with a predicted position of the vehicle at a first time, based at least in part on the trajectory; determining a second region associated with a predicted position of the object at the first time, based at least in part on the perturbed trajectory; and determining an overlap between the first region and the second region.
determining a first region associated with a predicted position of the vehicle at a first time, based at least in part on the trajectory;
determining a second region associated with a predicted position of the object at the first time, based at least in part on the perturbed trajectory; and
Ref US20210269065A1: Claims:
A method comprising: receiving sensor data associated with an environment of a first vehicle; detecting, based at least in part on the sensor data, a second vehicle proximate the first vehicle, wherein the second vehicle is substantially perpendicular to the first vehicle; determining, based at least in part on the sensor data, an attribute associated with the environment or the second vehicle that is indicative of whether the second vehicle is going to enter a lane region in front of the first vehicle; determining, based at least in part on the attribute, an indication whether the second vehicle will enter the lane region in front of the first vehicle; and determining an instruction for controlling the first vehicle based at least in part on the indication.
receiving sensor data associated with an environment of a first vehicle;
detecting, based at least in part on the sensor data, a second vehicle proximate the first vehicle, wherein the second vehicle is substantially perpendicular to the first vehicle;
determining, based at least in part on the sensor data, an attribute associated with the environment or the second vehicle that is indicative of whether the second vehicle is going to enter a lane region in front of the first vehicle;
determining, based at least in part on the attribute, an indication whether the second vehicle will enter the lane region in front of the first vehicle; and
determining an instruction for controlling the first vehicle based at least in part on the indication.
The method as claim 1 recites, wherein the second vehicle is substantially perpendicular to the first vehicle based at least in part on an angle between the second vehicle and the first vehicle being within a designated offset of 90 degrees.
The method as claim 2 recites, wherein the designated offset is 50 degrees.
The method as claim 1 recites, wherein the second vehicle is positioned in a parking spot, driveway, or alley that is substantially perpendicular to the first vehicle.
The method as claim 1 recites, wherein the second vehicle is associated with a different direction of travel than the first vehicle.
The method as claim 1 recites, wherein the attribute comprises at least one of: a position of the second vehicle relative to the first vehicle; an instantaneous velocity of the second vehicle; an indication of whether a driver is in the second vehicle; an indication of whether a door associated with the second vehicle is open or closed; an indication of whether an engine of the second vehicle is in a running state or off state; an indication of whether a brake light of the second vehicle is illuminated; an indication of whether a headlight of the second vehicle is illuminated; an indication of whether a reverse light of the second vehicle is illuminated; or an indication of whether a blinker of the second vehicle is illuminated.
a position of the second vehicle relative to the first vehicle;
an instantaneous velocity of the second vehicle;
an indication of whether a driver is in the second vehicle;
an indication of whether a door associated with the second vehicle is open or closed;
an indication of whether an engine of the second vehicle is in a running state or off state;
an indication of whether a brake light of the second vehicle is illuminated;
an indication of whether a headlight of the second vehicle is illuminated;
an indication of whether a reverse light of the second vehicle is illuminated; or
an indication of whether a blinker of the second vehicle is illuminated.
A system comprising: one or more processors; and one or more non-transitory computer-readable media storing instructions, that when executed by the one or more processors, cause the system to perform operations comprising: receiving sensor data associated with an environment of a first vehicle; determining, based at least in part on the sensor data, that a second vehicle proximate the first vehicle is predicted to enter a lane region in front of the first vehicle; and determining an instruction for controlling the first vehicle based at least in part on determining that the second vehicle proximate the first vehicle is predicted to enter the lane region in front of the first vehicle.
one or more processors; and
one or more non-transitory computer-readable media storing instructions, that when executed by the one or more processors, cause the system to perform operations comprising: receiving sensor data associated with an environment of a first vehicle; determining, based at least in part on the sensor data, that a second vehicle proximate the first vehicle is predicted to enter a lane region in front of the first vehicle; and determining an instruction for controlling the first vehicle based at least in part on determining that the second vehicle proximate the first vehicle is predicted to enter the lane region in front of the first vehicle.
determining, based at least in part on the sensor data, that a second vehicle proximate the first ve
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