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    SYSTEM AND METHOD OF PREDICTING HUMAN INTERACTION WITH VEHICLES
    2.
    发明申请
    SYSTEM AND METHOD OF PREDICTING HUMAN INTERACTION WITH VEHICLES 有权

    公开(公告)号：US20210182605A1

    公开(公告)日：2021-06-17

    申请号：US17190631

    申请日：2021-03-03

    Applicant: Perceptive Automata, Inc.

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

    IPC: G06K9/62 ,  G06N3/08 ,  G06N3/04 ,  G06K9/00 ,  G08G1/16 ,  G08G1/04 ,  G05D1/00 ,  B60W30/00

    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.

    SCENARIO BASED MONITORING AND CONTROL OF AUTONOMOUS VEHICLES
    4.
    发明公开
    SCENARIO BASED MONITORING AND CONTROL OF AUTONOMOUS VEHICLES 审中-公开

    公开(公告)号：US20230347931A1

    公开(公告)日：2023-11-02

    申请号：US18308626

    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

    IPC: B60W60/00 ,  G06V20/56 ,  B60W40/04

    CPC classification number: B60W60/001 ,  G06V20/56 ,  B60W40/04 ,  B60W2420/42 ,  B60W2554/4041

    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.

    Probabilistic neural network for predicting hidden context of traffic entities for autonomous vehicles
    5.
    发明授权
    Probabilistic neural network for predicting hidden context of traffic entities for autonomous vehicles 有权

    公开(公告)号：US11467579B2

    公开(公告)日：2022-10-11

    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.

    SCENARIO IDENTIFICATION FOR VALIDATION AND TRAINING OF MACHINE LEARNING BASED MODELS FOR AUTONOMOUS VEHICLES
    6.
    发明申请
    SCENARIO IDENTIFICATION FOR VALIDATION AND TRAINING OF MACHINE LEARNING BASED MODELS FOR AUTONOMOUS VEHICLES 有权

    公开(公告)号：US20210356968A1

    公开(公告)日：2021-11-18

    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: G05D1/02 ,  G06K9/00 ,  G06K9/62 ,  G06K9/46 ,  B60W60/00 ,  G06N20/00 ,  G06N5/04

    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.

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