公开(公告)号：US20200241545A1

公开(公告)日：2020-07-30

申请号：US16777386

申请日：2020-01-30

Applicant: Perceptive Automata, Inc.

Inventor： Samuel English Anthony

IPC: G05D1/02 ,  B60W30/095 ,  B60W30/09 ,  G05D1/00 ,  B60W40/09 ,  G06N20/00

Abstract: An autonomous vehicle uses machine learning based models to predict hidden context attributes associated with traffic entities. The system uses the hidden context to predict behavior of people near a vehicle in a way that more closely resembles how human drivers would judge the behavior. The system determines an activation threshold value for a braking system of the autonomous vehicle based on the hidden context. The system modifies a world model based on the hidden context predicted by the machine learning based model. The autonomous vehicle is safely navigated, such that the vehicle stays at least a threshold distance away from traffic entities.

公开(公告)号：US20200180647A1

公开(公告)日：2020-06-11

申请号：US16709790

申请日：2019-12-10

Applicant: Perceptive Automata, Inc.

Inventor： Samuel English Anthony

IPC: B60W50/00 ,  G05D1/00 ,  B60W40/04

Abstract: A system performs modeling and simulation of non-stationary traffic entities for testing and development of modules used in an autonomous vehicle system. The system uses a machine learning based model that predicts hidden context attributes for traffic entities that may be encountered by a vehicle in traffic. The system generates simulation data for testing and development of modules that help navigate autonomous vehicles. The generated simulation data may be image or video data including representations of traffic entities, for example, pedestrians, bicyclists, and other vehicles. The system may generate simulation data using generative adversarial neural networks.

公开(公告)号：US11987272B2

公开(公告)日：2024-05-21

申请号：US17190619

申请日：2021-03-03

Applicant: Perceptive Automata, Inc.

Inventor： Samuel English Anthony ,  Kshitij Misra ,  Avery Wagner Faller

IPC: G06N3/08 ,  B60W30/00 ,  B60W60/00 ,  G05D1/00 ,  G06F18/214 ,  G06F18/40 ,  G06N3/04 ,  G06N3/084 ,  G06V10/778 ,  G06V20/40 ,  G06V20/58 ,  G06V40/20 ,  G08G1/04 ,  G08G1/16 ,  G06N5/01 ,  G06N20/10 ,  G06V10/62

CPC classification number: B60W60/00274 ,  B60W30/00 ,  G05D1/0088 ,  G06F18/214 ,  G06F18/41 ,  G06N3/04 ,  G06N3/08 ,  G06N3/084 ,  G06V10/7784 ,  G06V20/41 ,  G06V20/58 ,  G06V40/20 ,  G08G1/04 ,  G08G1/166 ,  G05D2201/0213 ,  G06N5/01 ,  G06N20/10 ,  G06V10/62

Abstract: Systems and methods for predicting user interaction with vehicles. A computing device receives an image and a video segment of a road scene, the first at least one of an image and a video segment being taken from a perspective of a participant in the road scene and then generates stimulus data based on the image and the video segment. Stimulus data is transmitted to a user interface and response data is received, which includes at least one of an action and a likelihood of the action corresponding to another participant in the road scene. The computing device aggregates a subset of the plurality of response data to form statistical data and a model is created based on the statistical data. The model is applied to another image or video segment and a prediction of user behavior in the another image or video segment is generated.

公开(公告)号：US11919545B2

公开(公告)日：2024-03-05

申请号：US17321309

申请日：2021-05-14

Applicant: Perceptive Automata, Inc.

Inventor： Jeffrey D. Zaremba ,  Till S. Hartmann ,  Samuel English Anthony

IPC: B60W60/00 ,  G05D1/00 ,  G05D1/02 ,  G06F18/21 ,  G06F18/2113 ,  G06F18/214 ,  G06F18/40 ,  G06N3/08 ,  G06N5/04 ,  G06N20/00 ,  G06V10/764 ,  G06V10/82 ,  G06V20/40 ,  G06V20/56 ,  G06V20/58 ,  G06V40/20

CPC classification number: B60W60/00272 ,  B60W60/001 ,  G05D1/0221 ,  G05D1/0246 ,  G06F18/2113 ,  G06F18/214 ,  G06F18/217 ,  G06F18/40 ,  G06N3/08 ,  G06N5/04 ,  G06N20/00 ,  G06V10/764 ,  G06V10/82 ,  G06V20/41 ,  G06V20/56 ,  G06V20/58 ,  G06V40/23 ,  B60W2420/42 ,  B60W2420/52 ,  B60W2552/05 ,  B60W2554/4029 ,  B60W2554/4041 ,  B60W2554/4044 ,  B60W2554/4045 ,  B60W2554/4046 ,  B60W2554/408 ,  B60W2554/801 ,  G05D2201/0213

Abstract: A system uses a machine learning based model to determine attributes describing states of mind and behavior of traffic entities in video frames captured by an autonomous vehicle. The system classifies video frames according to traffic scenarios depicted, where each scenario is associated with a filter based on vehicle attributes, traffic attributes, and road attributes. The system identifies a set of video frames associated with ground truth scenarios for validating the accuracy of the machine learning based model and predicts attributes of traffic entities in the video frames. The system analyzes video frames captured after the set of video frames to determine actual attributes of the traffic entities. Based on a comparison of the predicted attributes and actual attributes, the system determines a likelihood of the machine learning based model making accurate predictions and uses the likelihood to generate a navigation action table for controlling the autonomous vehicle.

公开(公告)号：US20230351772A1

公开(公告)日：2023-11-02

申请号：US18308622

申请日：2023-04-27

Applicant: Perceptive Automata, Inc.

Inventor： Sonia Poltoraski ,  Till S. Hartman ,  Jeffrey Donald Zaremba ,  Samuel English Anthony ,  Chuan Yen Ian Goh ,  Omar Al Assad

IPC: G06V20/58 ,  B60W60/00 ,  G06V10/774 ,  G06V20/40

CPC classification number: G06V20/58 ,  B60W60/0027 ,  G06V10/774 ,  G06V20/40 ,  B60W2420/42 ,  G06V10/82

Abstract: A system evaluates modifications to components of an autonomous vehicle (AV) stack. The system receives driving recommendations traffic scenarios based on user annotations of video frames showing each traffic scenario. For each traffic scenario, the system predicts driving recommendations based on the AV stack. The system determines a measure of quality of driving recommendation by comparing predicted driving recommendations based on the AV stack with the driving recommendations received for the traffic scenario. The measure of quality of driving recommendation is used for evaluating components of the AV stack. The system determines a driving recommendation for an AV corresponding to ranges of SOMAI (state of mind) score and sends signals to controls of the autonomous vehicle to navigate the autonomous vehicle according to the driving recommendation. The system identifies additional training data for training machine learning model based on the measure of driving quality.

公开(公告)号：US20230347932A1

公开(公告)日：2023-11-02

申请号：US18308634

申请日：2023-04-27

Applicant: Perceptive Automata, Inc.

Inventor： Jeffrey Donald Zaremba ,  Chuan Yen Ian Goh ,  Omar Al Assad ,  Till S. Hartman ,  Sonia Poltoraski ,  Samuel English Anthony ,  James Gowers

IPC: B60W60/00 ,  G05B13/02 ,  G08G1/01 ,  B60W40/06 ,  B60W40/12

CPC classification number: B60W60/001 ,  G05B13/0265 ,  G08G1/0125 ,  B60W40/06 ,  B60W40/12 ,  B60W2552/00

Abstract: A system evaluates modifications to components of an autonomous vehicle (AV) stack. The system receives driving recommendations traffic scenarios based on user annotations of video frames showing each traffic scenario. For each traffic scenario, the system predicts driving recommendations based on the AV stack. The system determines a measure of quality of driving recommendation by comparing predicted driving recommendations based on the AV stack with the driving recommendations received for the traffic scenario. The measure of quality of driving recommendation is used for evaluating components of the AV stack. The system determines a driving recommendation for an AV corresponding to ranges of SOMAI (state of mind) score and sends signals to controls of the autonomous vehicle to navigate the autonomous vehicle according to the driving recommendation. The system identifies additional training data for training machine learning model based on the measure of driving quality.

公开(公告)号：US11667301B2

公开(公告)日：2023-06-06

申请号：US16709788

申请日：2019-12-10

Applicant: Perceptive Automata, Inc.

Inventor： Kshitij Misra ,  Samuel English Anthony

IPC: G01C22/00 ,  G05D1/00 ,  B60W50/00 ,  B60W40/04 ,  B60W60/00 ,  G06N3/08 ,  G08G1/01 ,  G06V20/56

CPC classification number: B60W50/0097 ,  B60W40/04 ,  B60W60/0011 ,  B60W60/0015 ,  G05D1/0088 ,  G06N3/08 ,  G06V20/56 ,  G08G1/0125 ,  G08G1/0145 ,  B60W2554/00 ,  G05D2201/0213

Abstract: A system performs modeling and simulation of non-stationary traffic entities for testing and development of modules used in an autonomous vehicle system. The system uses a machine learning based model that predicts hidden context attributes for traffic entities that may be encountered by a vehicle in traffic. The system generates simulation data for testing and development of modules that help navigate autonomous vehicles. The generated simulation data may be image or video data including representations of traffic entities, for example, pedestrians, bicyclists, and other vehicles. The system may generate simulation data using generative adversarial neural networks.

公开(公告)号：US20220138491A1

公开(公告)日：2022-05-05

申请号：US17468516

申请日：2021-09-07

Applicant: Perceptive Automata Inc.

Inventor： Samuel English Anthony ,  Kshitij Misra ,  Avery Wagner Faller

IPC: G06K9/62 ,  G06N3/08 ,  G08G1/16 ,  G06V20/40 ,  G05D1/00 ,  G06V40/20 ,  B60W30/00 ,  G06N3/04 ,  G08G1/04 ,  G06V20/58 ,  G06N20/10 ,  G06N5/00

Abstract: Systems and methods for predicting user interaction with vehicles. A computing device receives an image and a video segment of a road scene, the first at least one of an image and a video segment being taken from a perspective of a participant in the road scene and then generates stimulus data based on the image and the video segment. Stimulus data is transmitted to a user interface and response data is received, which includes at least one of an action and a likelihood of the action corresponding to another participant in the road scene. The computing device aggregates a subset of the plurality of response data to form statistical data and a model is created based on the statistical data. The model is applied to another image or video segment and a prediction of user behavior in the another image or video segment is generated.

公开(公告)号：USD928177S1

公开(公告)日：2021-08-17

申请号：US29694707

申请日：2019-06-12

Applicant: Perceptive Automata, Inc.

Designer： Avery Wagner Faller ,  Samuel English Anthony

公开(公告)号：US20200249677A1

公开(公告)日：2020-08-06

申请号：US16783845

申请日：2020-02-06

Applicant: Perceptive Automata, Inc.

Inventor： Jacob Reinier Maat ,  Samuel English Anthony

IPC: G05D1/00 ,  G06N7/00 ,  G06N3/08 ,  G05D1/02 ,  B60W60/00

Abstract: An autonomous vehicle uses probabilistic neural networks to predict hidden context attributes associated with traffic entities. The hidden context represents behavior of the traffic entities in the traffic. The probabilistic neural network is configured to receive an image of traffic as input and generate output representing hidden context for a traffic entity displayed in the image. The system executes the probabilistic neural network to generate output representing hidden context for traffic entities encountered while navigating through traffic. The system determines a measure of uncertainty for the output values. The autonomous vehicle uses the measure of uncertainty generated by the probabilistic neural network during navigation.