Autonomous vehicle park-and-go scenario design

    公开(公告)号:US11577758B2

    公开(公告)日:2023-02-14

    申请号:US16734146

    申请日:2020-01-03

    申请人: Baidu USA LLC

    摘要: In one embodiment, when an autonomous driving vehicle (ADV) is parked, the ADV can determine, based on criteria, whether to operate in an open-space mode or an on-lane mode. The criteria can include whether the ADV is within a threshold distance and threshold heading relative to a vehicle lane. If the criteria are not satisfied, then the ADV can enter the open-space mode. While in the open-space mode, the ADV can maneuver it is within the threshold distance and the threshold heading relative to the vehicle lane. In response to the criteria being satisfied, the ADV can enter and operate in the on-lane mode for the ADV to resume along the vehicle lane.

    Static-state curvature error compensation control logic for autonomous driving vehicles

    公开(公告)号:US11518404B2

    公开(公告)日:2022-12-06

    申请号:US16826707

    申请日:2020-03-23

    申请人: Baidu USA LLC

    摘要: In one embodiment, static-state curvature error compensation control logic for autonomous driving vehicles (ADV) receives planning and control data associated with the ADV, including a planned steering angle and a planned speed. A steering command is generated based on a current steering angle and the planned steering angle of the ADV. A throttle command is generated based on the planned speed in view of a current speed of the ADV. A curvature error is calculated based on a difference between the current steering angle and the planned steering angle. The steering command is issued to the ADV while withholding the throttle command, in response to determining that the curvature error is greater than a predetermined curvature threshold, such that the steering angle of the ADV is adjusted in view of the planned steering angle without acceleration.

    Model reference adaptive control algorithm to address the vehicle actuation dynamics

    公开(公告)号:US11492008B2

    公开(公告)日:2022-11-08

    申请号:US16797833

    申请日:2020-02-21

    申请人: Baidu USA LLC

    摘要: Systems and methods are disclosed for reducing second order dynamics delays in a control subsystem (e.g. throttle, braking, or steering) in an autonomous driving vehicle (ADV). A control input is received from an ADV perception and planning system. The control input is translated in a control command to a control subsystem of the ADV. A reference actuation output is obtained from a storage of the ADV. The reference actuation output is a smoothed output that accounts for second order actuation dynamic delays attributable to the control subsystem actuator. Based on a difference between the control input and the reference actuation output, adaptive gains are determined and applied to the input control signal to reduce error between the control output and the reference actuation output.

    Segmenting a parking trajectory to control an autonomous driving vehicle to park

    公开(公告)号:US11485353B2

    公开(公告)日:2022-11-01

    申请号:US16399579

    申请日:2019-04-30

    申请人: Baidu USA LLC

    IPC分类号: B60W30/06 G05D1/00 G06V20/58

    摘要: In one embodiment, a computer-implemented method of autonomously parking an autonomous driving vehicle, includes generating environment descriptor data describing a driving environment surrounding the autonomous driving vehicle (ADV), including identifying a parking space and one or more obstacles within a predetermined proximity of the ADV, generating a parking trajectory of the ADV based on the environment descriptor data to autonomously park the ADV into the parking space, including optimizing the parking trajectory in view of the one or more obstacles, segmenting the parking trajectory into one or more trajectory segments based on a vehicle state of the ADV, and controlling the ADV according to the one or more trajectory segments of the parking trajectory to autonomously park the ADV into the parking space without collision with the one or more obstacles.

    Online agent using reinforcement learning to plan an open space trajectory for autonomous vehicles

    公开(公告)号:US11467591B2

    公开(公告)日:2022-10-11

    申请号:US16413332

    申请日:2019-05-15

    申请人: Baidu USA LLC

    IPC分类号: G05D1/02 G06N3/08 G06N3/04

    摘要: In one embodiment, a system uses an actor-critic reinforcement learning model to generate a trajectory for an autonomous driving vehicle (ADV) in an open space. The system perceives an environment surrounding an ADV. The system applies a RL algorithm to an initial state of a planning trajectory based on the perceived environment to determine a plurality of controls for the ADV to advance to a plurality of trajectory states based on map and vehicle control information for the ADV. The system determines a reward prediction by the RL algorithm for each of the plurality of controls in view of a target destination state. The system generates a first trajectory from the trajectory states by maximizing the reward predictions to control the ADV autonomously according to the first trajectory.

    Data collection automation system
    26.
    发明授权

    公开(公告)号:US11462060B2

    公开(公告)日:2022-10-04

    申请号:US16397633

    申请日:2019-04-29

    申请人: Baidu USA LLC

    摘要: An autonomous driving vehicle (ADV) receives instructions for a human test driver to drive the ADV in manual mode and to collect a specified amount of driving data for one or more specified driving categories. As the user drivers the ADV in manual mode, driving data corresponding to the one or more driving categories is logged. A user interface of the ADV displays the one or more driving categories that the human driver is instructed collect data upon, and a progress indicator for each of these categories as the human driving progresses. The driving data is uploaded to a server for machine learning. If the server machine learning achieves a threshold grading amount of the uploaded data to variables of a dynamic self-driving model, then the server generates an ADV self-driving model, and distributes the model to one or more ADVs that are navigated in the self-driving mode.

    STATIC-STATE CURVATURE ERROR COMPENSATION CONTROL LOGIC FOR AUTONOMOUS DRIVING VEHICLES

    公开(公告)号:US20210291855A1

    公开(公告)日:2021-09-23

    申请号:US16826707

    申请日:2020-03-23

    申请人: Baidu USA LLC

    摘要: In one embodiment, static-state curvature error compensation control logic for autonomous driving vehicles (ADV) receives planning and control data associated with the ADV, including a planned steering angle and a planned speed. A steering command is generated based on a current steering angle and the planned steering angle of the ADV. A throttle command is generated based on the planned speed in view of a current speed of the ADV. A curvature error is calculated based on a difference between the current steering angle and the planned steering angle. The steering command is issued to the ADV while withholding the throttle command, in response to determining that the curvature error is greater than a predetermined curvature threshold, such that the steering angle of the ADV is adjusted in view of the planned steering angle without acceleration.

    DISTRIBUTIONAL EXPERT DEMONSTRATIONS FOR AUTONOMOUS DRIVING

    公开(公告)号:US20230406345A1

    公开(公告)日:2023-12-21

    申请号:US17843546

    申请日:2022-06-17

    申请人: Baidu USA LLC

    IPC分类号: B60W60/00 B60W50/00 B60W30/18

    摘要: The present disclosure provides methods and techniques for evaluating and improving algorithms for autonomous driving planning and control (PNC), using one or more metrics (e.g., similarity scores) computed based on expert demonstrations. For example, the one or more metrics allow for improving PNC based on human, as opposed to or in addition to optimizing certain oversimplified properties, such as the least distance or time, as an objective. When driving in certain scenarios, such as taking a turn, people may drive in a distributed probability pattern instead of in a uniform line (e.g., different speeds and different curvatures at the same corner). As such, there can be more than one “correct” control trajectory for an autonomous vehicle to perform in the same turn. Safety, comfort, speeds, and other criteria may lead to different preferences and judgment as to how well the controlled trajectory has been computed.