利索-全球专利检索

  1. 登录
  2. 注册

搜索结果

803 条记录 (耗时 0.026 秒)

    Random shift based path centering system for autonomous vehicles
    3.
    发明授权
    Random shift based path centering system for autonomous vehicles 有权

    公开(公告)号：US12085945B2

    公开(公告)日：2024-09-10

    申请号：US16927013

    申请日：2020-07-13

    申请人： Baidu USA LLC

    发明人： Fan Zhu

    IPC分类号： G05D1/00 ,  B60W60/00

    CPC分类号： G05D1/0221 ,  B60W60/0011 ,  G05D1/0088

    摘要： Embodiments of the present disclosures disclose a method and a system to generate a path planning trajectory with a random lateral shift for an autonomous driving vehicle (ADV). In one embodiment, a system generates a reference line to navigate the ADV from a start location to a destination location. The system determines a lateral shift distance value to shift a lane center for the reference line. The system generates a shifted trajectory using the reference line based on the lateral shift distance value. The system controls the ADV based on the shifted trajectory to navigate the ADV.

    DATA-DRIVEN PREDICTION-BASED SYSTEM AND METHOD FOR TRAJECTORY PLANNING OF AUTONOMOUS VEHICLES
    5.
    发明公开
    DATA-DRIVEN PREDICTION-BASED SYSTEM AND METHOD FOR TRAJECTORY PLANNING OF AUTONOMOUS VEHICLES 审中-公开

    公开(公告)号：US20240288868A1

    公开(公告)日：2024-08-29

    申请号：US18507038

    申请日：2023-11-11

    申请人： TUSIMPLE, INC.

    发明人： Xing SUN ,  Wutu LIN ,  Liu LIU ,  Kai-Chieh MA ,  Zijie XUAN ,  Yufei ZHAO

    IPC分类号： G05D1/00 ,  B62D15/02

    CPC分类号： G05D1/0221 ,  B62D15/0255 ,  B62D15/026 ,  B62D15/0265

    摘要： A data-driven prediction-based system and method for trajectory planning of autonomous vehicles are disclosed. A particular embodiment includes: generating a first suggested trajectory for an autonomous vehicle; generating predicted resulting trajectories of proximate agents using a prediction module; scoring the first suggested trajectory based on the predicted resulting trajectories of the proximate agents; generating a second suggested trajectory for the autonomous vehicle and generating corresponding predicted resulting trajectories of proximate agents, if the score of the first suggested trajectory is below a minimum acceptable threshold; and outputting a suggested trajectory for the autonomous vehicle wherein the score corresponding to the suggested trajectory is at or above the minimum acceptable threshold.

    Rare event simulation in autonomous vehicle motion planning
    8.
    发明授权
    Rare event simulation in autonomous vehicle motion planning 有权

    公开(公告)号：US12019449B2

    公开(公告)日：2024-06-25

    申请号：US17178333

    申请日：2021-02-18

    申请人： Argo AI, LLC

    发明人： Thomas Muehlenstaedt ,  Vincent Aravantinos

    IPC分类号： G05D1/00 ,  G06F30/15 ,  G06F30/20

    CPC分类号： G05D1/0221 ,  G05D1/0214 ,  G06F30/15 ,  G06F30/20

    摘要： Methods of identifying corner case simulation scenarios that are used to train an autonomous vehicle motion planning model are disclosed. A system selects a scene that includes data captured by one or more vehicles over a time period. The data includes one or more actors that the vehicle's sensors perceived over the time period in a real-world environment. The system selects a scene that includes a safety threshold violation, and it identifies the trajectory of an actor that participated in the violation. The system generates simulated scenes that alter the trajectory of the actor in the selected scene, selects simulated scenes that are more likely to occur in the real world and that may include safety threshold violations that go beyond any that may be found in the original scene, and uses the selected simulated scenes to train an autonomous vehicle motion planning model.

    Neural network based vehicle dynamics model
    9.
    发明授权
    Neural network based vehicle dynamics model 有权

    公开(公告)号：US12007778B2

    公开(公告)日：2024-06-11

    申请号：US18094363

    申请日：2023-01-08

    申请人： TuSimple, Inc.

    发明人： Jun Chen ,  Wutu Lin ,  Liu Liu

    IPC分类号： G06F30/20 ,  G05D1/00 ,  G06N3/00

    CPC分类号： G05D1/0221 ,  G05D1/0088 ,  G05D1/0274 ,  G05D1/0285 ,  G06N3/00

    摘要： A system and method for implementing a neural network based vehicle dynamics model are disclosed. A particular embodiment includes: training a machine learning system with a training dataset corresponding to a desired autonomous vehicle simulation environment; receiving vehicle control command data and vehicle status data, the vehicle control command data not including vehicle component types or characteristics of a specific vehicle; by use of the trained machine learning system, the vehicle control command data, and vehicle status data, generating simulated vehicle dynamics data including predicted vehicle acceleration data; providing the simulated vehicle dynamics data to an autonomous vehicle simulation system implementing the autonomous vehicle simulation environment; and using data produced by the autonomous vehicle simulation system to modify the vehicle status data for a subsequent iteration.