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    Determining respective impacts of agents
    23.
    发明授权
    Determining respective impacts of agents 有权

    公开(公告)号：US11829149B1

    公开(公告)日：2023-11-28

    申请号：US17750196

    申请日：2022-05-20

    Applicant: Waymo LLC

    Inventor： Khaled Refaat ,  Kai Ding ,  Stephane Ross

    IPC: G05D1/02 ,  G01C21/34

    CPC classification number: G05D1/0219 ,  G01C21/3446 ,  G01C21/3492 ,  G05D1/0221

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining respective importance scores for a plurality of agents in a vicinity of an autonomous vehicle navigating through an environment. The respective importance scores characterize a relative impact of each agent on planned trajectories generated by a planning subsystem of the autonomous vehicle. In one aspect, a method comprises providing different states of an environment as input to the planning subsystem and obtaining as output from the planning subsystem corresponding planned trajectories. Importance scores for the one or more agents that are in one state but not in the other are determined based on a measure of difference between the planned trajectories.

    Training trajectory scoring neural networks to accurately assign scores
    26.
    发明授权
    Training trajectory scoring neural networks to accurately assign scores 有权

    公开(公告)号：US11586931B2

    公开(公告)日：2023-02-21

    申请号：US16671019

    申请日：2019-10-31

    Applicant: Waymo LLC

    Inventor： Khaled Refaat ,  Kai Ding ,  Stephane Ross

    IPC: G06N3/08 ,  G06N3/084 ,  G06N3/04 ,  G05D1/02 ,  G05D1/00

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a neural network having a plurality of sub neural networks to assign respective confidence scores to one or more candidate future trajectories for an agent. Each confidence score indicates a predicted likelihood that the agent will move along the corresponding candidate future trajectory in the future. In one aspect, a method includes using the first sub neural network to generate a training intermediate representation; using the second sub neural network to generate respective training confidence scores; using a trajectory generation neural network to generate a training trajectory generation output; computing a first loss and a second loss; and determining an update to the current values of the parameters of the first and second sub neural networks.

    Predicting Behaviors of Road Agents Using Intermediate Intention Signals
    28.
    发明申请
    Predicting Behaviors of Road Agents Using Intermediate Intention Signals 有权

    公开(公告)号：US20220161824A1

    公开(公告)日：2022-05-26

    申请号：US17101842

    申请日：2020-11-23

    Applicant: Waymo LLC

    Inventor： Khaled Refaat

    IPC: B60W60/00 ,  G01S17/931 ,  G01S17/58 ,  G01S13/931 ,  G01S13/58

    Abstract: An autonomous vehicle includes sensor subsystem(s) that output a sensor signal. A perception subsystem (i) detects an agent in a vicinity of the autonomous vehicle and (ii) generates a motion signal that describes at least one of a past motion or a present motion of the agent. An intention prediction subsystem processes the sensor signal to generate an intention signal that describes at least one intended action of the agent. A behavior prediction subsystem processes the motion signal and the intention signal to generate a behavior prediction signal that describes at least one predicted behavior of the agent. A planner subsystem processes the behavior prediction signal to plan a driving decision for the autonomous vehicle.

    AGENT PRIORITIZATION FOR AUTONOMOUS VEHICLES
    29.
    发明申请
    AGENT PRIORITIZATION FOR AUTONOMOUS VEHICLES 有权

    公开(公告)号：US20210269023A1

    公开(公告)日：2021-09-02

    申请号：US17320727

    申请日：2021-05-14

    Applicant: Waymo LLC

    Inventor： Kai Ding ,  Khaled Refaat ,  Stephane Ross

    IPC: B60W30/095 ,  G05D1/02 ,  G05D1/00

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for identifying high-priority agents in the vicinity of a vehicle. In one aspect, a method comprises processing an input that characterizes a trajectory of the vehicle in an environment using an importance scoring model to generate an output that defines a respective importance score for each of a plurality of agents in the environment in the vicinity of the vehicle. The importance score for an agent characterizes an estimated impact of the agent on planning decisions generated by a planning system of the vehicle which plans a future trajectory of the vehicle. The high-priority agents are identified as a proper subset of the plurality of agents with the highest importance scores.

    Trajectory representation in behavior prediction systems
    30.
    发明授权
    Trajectory representation in behavior prediction systems 有权

    公开(公告)号：US11003189B2

    公开(公告)日：2021-05-11

    申请号：US16922798

    申请日：2020-07-07

    Applicant: Waymo LLC

    Inventor： Khaled Refaat ,  Stephane Ross

    IPC: G05D1/02 ,  G06N20/00 ,  G05D1/00 ,  G06N5/04

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a representation of a trajectory of a target agent in an environment. In one aspect, the representation of the trajectory of the target agent in the environment is a concatenation of a plurality of channels, where each channel is represented as a two-dimensional array of data values. Each position in each channel corresponds to a respective spatial position in the environment, and corresponding positions in different channels correspond to the same spatial position in the environment. The channels include a time channel and a respective motion channel corresponding to each motion parameter in a predetermined set of motion parameters.

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