SIMULATION OBSTACLE VEHICLES WITH DRIVING STYLES

    公开(公告)号:US20230205951A1

    公开(公告)日:2023-06-29

    申请号:US17645860

    申请日:2021-12-23

    申请人: Baidu USA LLC

    IPC分类号: G06F30/27

    CPC分类号: G06F30/27 B60W60/0015

    摘要: According to various embodiments, described herein is a method of creating a simulation environment with multiple simulation obstacle vehicles, each with a different human-like driving style. Training datasets with different driving styles can be collected from individual human drivers, and can be combined to generate mixed datasets, each mixed dataset including only data of a particular driving style. Multiple learning-based motion planner critics can be trained using the mixed datasets, and can be used to tune multiple motion planners. Each tuned motion planner can have a different human-like driving style, and can be installed in one of multiple simulation obstacle vehicles. The simulation obstacle vehicles with different human-like driving styles can be deployed to the simulation environment to make the simulation environment more resemble a real-world driving environment.

    DECISION CONSISTENCY PROFILER FOR AN AUTONOMOUS DRIVING VEHICLE

    公开(公告)号:US20230060776A1

    公开(公告)日:2023-03-02

    申请号:US17446648

    申请日:2021-09-01

    申请人: Baidu USA LLC

    摘要: Embodiments of the invention are intended to evaluate the performance of a planning module of the ADV in terms of decision consistency in addition to other metrics, such as comfort, latency, controllability, and safety. In one embodiment, an exemplary method includes receiving, at an autonomous driving simulation platform, a record file recorded by the ADV that was automatically driving on a road segment; simulating operations of a dynamic model of the ADV in the autonomous driving simulation platform during one or more driving scenarios on the road segment based on the record file. The method further includes performing a comparison between each planned trajectory generated by a planning module of the dynamic model after an initial period of time with each trajectory stored in a buffer; and modifying a performance score generated by a planning performance profiler in the autonomous driving simulation platform based on a result of the comparison.

    LEARNING BASED CONTROLLER FOR AUTONOMOUS DRIVING

    公开(公告)号:US20210291862A1

    公开(公告)日:2021-09-23

    申请号:US16823141

    申请日:2020-03-18

    申请人: Baidu USA LLC

    IPC分类号: B60W60/00

    摘要: In one embodiment, a control command is generated with an MPC controller, the MPC controller including a cost function with weights associated with cost terms of the cost function. The control command is applied to a dynamic model of an autonomous driving vehicle (ADV) to simulate behavior of the ADV. One or more of the weights are based on evaluation of the dynamic model in response to the control command, resulting in an adjusted cost function of the MPC controller. Another control command is generated with the MPC controller having the adjusted cost function. This second control command can be used to effect movement of the ADV.

    PATH OPTIMIZATION BASED ON CONSTRAINED SMOOTHING SPLINE FOR AUTONOMOUS DRIVING VEHICLES

    公开(公告)号:US20190086925A1

    公开(公告)日:2019-03-21

    申请号:US15707253

    申请日:2017-09-18

    申请人: Baidu USA LLC

    IPC分类号: G05D1/02 G05D1/00

    摘要: According to some embodiments, a system segments a first path trajectory selected from an initial location of the ADV into a number of path segments, where each path segment is represented by a polynomial function. The system selects an objective function in view of the polynomial functions of the path segments for smoothing connections between the path segments. The system defines a set of constraints to the polynomial functions based on adjacent path segments in view of at least a road boundary and an obstacle perceived by the ADV. The system performs a quadratic programming (QP) optimization on the objective function in view of the added constraints, such that an output of the objective function reaches a minimum. The system generates a second path trajectory representing a path trajectory with an optimized objective function based on the QP optimization to control the ADV autonomously.

    HUMAN-MACHINE INTERFACE (HMI) ARCHITECTURE
    37.
    发明申请

    公开(公告)号:US20190004510A1

    公开(公告)日:2019-01-03

    申请号:US15640867

    申请日:2017-07-03

    申请人: Baidu USA LLC

    摘要: An autonomous driving vehicle (ADV) is operated using a human-machine interface (HMI). The web server provides the HMI to a computing device in response to an input received from the computing device. An ADV command is entered into the HMI and passed to an interface of the web server. In response to receiving the ADV command, the web server calls a remote procedure call to a proxy server in a backend server for processing by a perception and control module of the ADV. Results of the ADV command are received by the web server interface and stored in results memory with a unique identifier. The web server interface opens a socket that accesses the results memory. If the results memory changes, the socket reads the results memory and provides the ADV command results to the HMI. Multiple HMIs can simultaneously communicate with the web server interface and socket.