OPTIMAL PATH LIBRARY FOR LOCAL PATH PLANNING OF AN AUTONOMOUS VEHICLE

    公开(公告)号:US20230244237A1

    公开(公告)日:2023-08-03

    申请号:US18186137

    申请日:2023-03-17

    Applicant: TUSIMPLE, INC.

    Abstract: Methods, systems and apparatus for autonomous vehicle path planning and path navigation are described. One example system includes an offline server configured to generate a library of optimal paths for navigating a geographic area, wherein the geographic area is represented as a grid node map and an orientation grid bin map and wherein the optimal paths correspond to paths between pairs of grid node pairs in the grid node map based on optimization criteria, a storage device on the autonomous vehicle for storing the library of optimal paths, and an online server located on the autonomous vehicle configured to access information from the library of optimal paths from the storage device based on a current position and a current heading of the autonomous vehicle, and navigating the autonomous vehicle through the geographic area based on the information.

    VEHICLE POWERTRAIN INTEGRATED PREDICTIVE DYNAMIC CONTROL FOR AUTONOMOUS DRIVING

    公开(公告)号:US20220242413A1

    公开(公告)日:2022-08-04

    申请号:US17164207

    申请日:2021-02-01

    Applicant: TUSIMPLE, INC.

    Abstract: Devices, systems, and methods for integrated predictive dynamic control of a vehicle powertrain in an autonomous vehicle are described. An example method for controlling a vehicle includes generating, based on performing an optimization on a blended smooth wheel domain fuel consumption map subject to a modified torque availability constraint, one or more wheel domain control commands, converting the one or more wheel domain control commands to one or more powertrain-executable engine domain control commands, and transmitting the one or more powertrain-executable engine domain control commands to a powertrain of the vehicle, the powertrain configured to operate a plurality of gears, wherein the one or more powertrain-executable engine domain control commands enable the vehicle to track a reference kinematic trajectory associated with a vehicle speed driving plan within a predetermined tolerance.

    SYSTEMS AND METHODS FOR AUTONOMOUS DRIVING BASED ON BOUNDED TRACKING

    公开(公告)号:US20240391490A1

    公开(公告)日:2024-11-28

    申请号:US18664712

    申请日:2024-05-15

    Applicant: TuSimple, Inc.

    Abstract: An example method for controlling a vehicle includes obtaining reference information relating to an operation parameter of the vehicle, the operation parameter describing mission waypoints of the vehicle at respective time points during which the vehicle is to traverse a path, the reference information including reference values of the operation parameter corresponding to the time points; obtaining context information of the vehicle that relates to a state of the vehicle during an operation of the vehicle at the respective time points or an environment enclosing the path; determining tolerable ranges of the operation parameter for the time points based on the reference information and the context information; obtaining penalty information relating to differences between respective tolerable ranges and corresponding values of a constraint at the time points; determining a control instruction based on the tolerable ranges and the penalty information; and operating the vehicle based on the control instruction.

    SYSTEMS AND METHODS FOR UNCERTAINTY PREDICTION IN AUTONOMOUS DRIVING

    公开(公告)号:US20240383486A1

    公开(公告)日:2024-11-21

    申请号:US18664793

    申请日:2024-05-15

    Applicant: TuSimple, Inc.

    Abstract: Devices, systems, and methods for controlling a vehicle are described. An example method for controlling a vehicle includes obtaining planning information relating to an intended operation of the vehicle over a prediction horizon; inputting the planning information into an uncertainty model to determine uncertainty information, wherein: the uncertainty model is trained using sample driving event data based on a multivariate probability prediction algorithm; and the uncertainty model is configured to predict the uncertainty information that relates to a deviation of an operation of the vehicle according to an intended control instruction from the intended operation, the intended control instruction being determined based on the planning information; generating a control instruction based on the planning information and the uncertainty information; and operating the vehicle based on the control instruction.

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