DP AND QP BASED DECISION AND PLANNING FOR AUTONOMOUS DRIVING VEHICLES

    公开(公告)号:US20190079523A1

    公开(公告)日:2019-03-14

    申请号:US15701411

    申请日:2017-09-11

    Applicant: Baidu USA LLC

    Abstract: According to some embodiments, a system calculates a first trajectory based on a map and a route information. The system generates a path profile based on the first trajectory, traffic rules, and an obstacle information describing one or more obstacles perceived by the ADV. The system generates a speed profile based on the path profile, where the speed profile includes, for each of the obstacles, a decision to yield or overtake the obstacle. The system performs a quadratic programming optimization on the path profile and the speed profile to identify an optimal path with optimal speeds. The system generates a second trajectory based on the optimal path and optimal speeds to control the ADV autonomously according to the second trajectory.

    ST-GRAPH LEARNING BASED DECISION FOR AUTONOMOUS DRIVING VEHICLE

    公开(公告)号:US20200209872A1

    公开(公告)日:2020-07-02

    申请号:US16233494

    申请日:2018-12-27

    Applicant: Baidu USA LLC

    Abstract: In one embodiment, a data processing system for an autonomous driving vehicle (ADV) includes a processor, and a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations. The operations include generating a station-time (ST) graph based on perception data obtained from one or more sensors of the ADV, the ST graph including representing a location of an obstacle at different points in time, obtaining a tensor based on the ST graph, the tensor including a plurality of layers, the plurality of layers including a first layer having data representing one or more obstacles on a path in which the ADV is moving, applying a machine-learning model to the plurality of layers of the tensor to generate a plurality of numerical values, the plurality of numerical values defining a potential path trajectory of the ADV, and determining a path trajectory of the ADV based on the plurality of numerical values.

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