Multistage Autonomous Vehicle Motion Planning

    公开(公告)号:US20240166237A1

    公开(公告)日:2024-05-23

    申请号:US18465132

    申请日:2023-09-11

    CPC classification number: B60W60/0011 B60W40/04

    Abstract: Example methods for multistage autonomous vehicle motion planning include obtaining sensor data descriptive of an environment of the autonomous vehicle; identifying one or more objects in the environment based on the sensor data; generating a plurality of candidate strategies, wherein each candidate strategy of the plurality of candidate strategies comprises a set of discrete decisions respecting the one or more objects, wherein generating the plurality of candidate strategies includes: determining that at least two strategies satisfy an equivalence criterion, such that the plurality of candidate strategies include at least one candidate strategy corresponding to an equivalence class representative of a plurality of different strategies that are based on different discrete decisions; determining candidate trajectories respectively for the plurality of candidate strategies; and initiating control of the autonomous vehicle based on a selected candidate trajectory.

    Multistage autonomous vehicle motion planning

    公开(公告)号:US11787439B1

    公开(公告)日:2023-10-17

    申请号:US17989898

    申请日:2022-11-18

    CPC classification number: B60W60/00 B60W40/04

    Abstract: Example methods for multistage autonomous vehicle motion planning include obtaining sensor data descriptive of an environment of the autonomous vehicle; identifying one or more objects in the environment based on the sensor data; generating a plurality of candidate strategies, wherein each candidate strategy of the plurality of candidate strategies comprises a set of discrete decisions respecting the one or more objects, wherein generating the plurality of candidate strategies includes: determining that at least two strategies satisfy an equivalence criterion, such that the plurality of candidate strategies include at least one candidate strategy corresponding to an equivalence class representative of a plurality of different strategies that are based on different discrete decisions; determining candidate trajectories respectively for the plurality of candidate strategies; and initiating control of the autonomous vehicle based on a selected candidate trajectory.

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