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    Intervention behavior prediction
    2.
    发明授权
    Intervention behavior prediction 有权

    公开(公告)号：US12071161B1

    公开(公告)日：2024-08-27

    申请号：US17858886

    申请日：2022-07-06

    申请人： Waymo LLC

    发明人： Khaled Refaat ,  Stéphane Ross

    IPC分类号： B60W60/00 ,  B60W40/04 ,  B60W50/00

    CPC分类号： B60W60/0011 ,  B60W40/04 ,  B60W50/0097 ,  B60W60/0015 ,  B60W60/00274 ,  B60W2554/4045 ,  B60W2554/4046

    摘要： Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for intervention behavior prediction. One of the methods includes receiving data characterizing a scene that includes a first agent and a second agent in an environment. A confounder prediction input generated from the data is processed using a confounder prediction model. A plurality of predicted conditional probability distributions is generated, wherein each predicted conditional probability distribution is conditioned on: (i) a planned intervention by the second agent, and (ii) the confounder variable belonging to a corresponding confounder class. An intervention behavior prediction for the first agent is generated based on the plurality of the predicted conditional probability distributions and the confounder distribution, wherein the intervention behavior prediction includes a probability distribution over the plurality of the possible behaviors for the first agent in reaction to the second agent performing the planned intervention.

    Behavior prediction of surrounding agents
    4.
    发明授权
    Behavior prediction of surrounding agents 有权

    公开(公告)号：US12051249B2

    公开(公告)日：2024-07-30

    申请号：US18216488

    申请日：2023-06-29

    申请人： Waymo LLC

    发明人： Mayank Bansal ,  Dragomir Anguelov

    IPC分类号： G06V20/58 ,  B60W60/00 ,  G06N3/045 ,  G06N3/047 ,  G06V10/764 ,  G06V10/82 ,  G06V20/10

    CPC分类号： G06V20/58 ,  B60W60/00259 ,  B60W60/00274 ,  G06N3/045 ,  G06N3/047 ,  G06V10/764 ,  G06V10/82 ,  G06V20/10

    摘要： Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting occupancies of agents. One of the methods includes obtaining scene data characterizing a current scene in an environment; and processing a neural network input comprising the scene data using a neural network to generate a neural network output, wherein: the neural network output comprises respective occupancy outputs corresponding to a plurality of agent types at one or more future time points; the occupancy output for each agent type at a first future time point comprises respective occupancy probabilities for a plurality of locations in the environment; and in the occupancy output for each agent type at the first future time point, the respective occupancy probability for each location characterizes a likelihood that an agent of the agent type will occupy the location at the first future time point.

    TRACKER TRAJECTORY VALIDATION
    6.
    发明公开
    TRACKER TRAJECTORY VALIDATION 审中-公开

    公开(公告)号：US20240208489A1

    公开(公告)日：2024-06-27

    申请号：US18087474

    申请日：2022-12-22

    申请人： Zoox, Inc.

    发明人： Taylor Scott CLAWSON ,  Brian Michael FILARSKY

    IPC分类号： B60W30/09 ,  B60W30/095 ,  B60W50/00 ,  B60W50/02 ,  B60W60/00

    CPC分类号： B60W30/09 ,  B60W30/0956 ,  B60W50/0097 ,  B60W50/0205 ,  B60W60/0011 ,  B60W60/00274 ,  B60W2050/0031 ,  B60W2556/20 ,  B60W2556/45

    摘要： Collision avoidance and error determination for a component of an autonomous vehicle comprising receiving a first trajectory, such as to return a vehicle to an intended trajectory, that a vehicle is predicted to follow, based on an offset between the vehicle and a second trajectory associated with the vehicle, such as a reference trajectory. The first trajectory predicts a first movement characteristic (e.g., a position) of the vehicle at a point in time. A second movement characteristic is received, representing an actual movement characteristic of the vehicle at that point in time. A first error between the first and second movement characteristics is determined. Based at least in part on the first error, performance of a model for generating trajectories that a vehicle is predicted to follow is validated.

    Obstacle prediction system for autonomous driving vehicles
    7.
    发明授权
    Obstacle prediction system for autonomous driving vehicles 有权

    公开(公告)号：US12017681B2

    公开(公告)日：2024-06-25

    申请号：US16817948

    申请日：2020-03-13

    申请人： Baidu USA LLC

    发明人： Fan Zhu

    IPC分类号： B60W60/00 ,  B60W30/095 ,  B60W40/04 ,  G06V20/58 ,  H04W4/40

    CPC分类号： B60W60/0011 ,  B60W30/0956 ,  B60W40/04 ,  B60W60/00274 ,  G06V20/58 ,  G06V20/584 ,  H04W4/40 ,  B60W2552/45 ,  B60W2554/4029

    摘要： Embodiments of a system/method is disclosed to operate an autonomous driving vehicle (ADV). In one embodiment, a system perceives a driving environment surrounding the ADV using a plurality of sensors mounted on the ADV including one or more obstacles. The system receives traffic signal information from one or more traffic indicators identified within a predetermined radius of the ADV. For each of the one or more obstacles, the system determines if the obstacle is situated on a lane with traffic flow coordinated by the one or more traffic indicators. The system predicts a behavior of the obstacle based on the traffic signal information for the lane. The system plans a trajectory based on the predicted behaviors for the one or more obstacles to control the ADV based on the planned trajectory.

    MIXED REALITY SIMULATION FOR AUTONOMOUS SYSTEMS
    8.
    发明公开
    MIXED REALITY SIMULATION FOR AUTONOMOUS SYSTEMS 审中-公开

    公开(公告)号：US20240157978A1

    公开(公告)日：2024-05-16

    申请号：US18506638

    申请日：2023-11-10

    申请人： WAABI Innovation Inc.

    发明人： Kelvin WONG ,  Simon SUO ,  Raquel URTASUN

    IPC分类号： B60W60/00 ,  G06F16/29

    CPC分类号： B60W60/00274 ,  G06F16/29

    摘要： A method includes obtaining, from sensor data, map data of a geographic region and multiple trajectories of multiple agents located in the geographic region. The agents and the map data have a corresponding physical location in the geographic region. The method further includes determining, for an agent, an agent route from a trajectory that corresponds to the agent, generating, by an encoder model, an interaction encoding that encodes the trajectories and the map data, and generating, from the interaction encoding, an agent attribute encoding of the agent and the agent route. The method further includes processing the agent attribute encoding to generate positional information for the agent, and updating the trajectory of the agent using the positional information to obtain an updated trajectory.