Great Wall Motor Company Limited

Duong Le

Kai Zhang

Zhichao Liu

Disclosed herein are methods and systems for motion planning in an autonomous vehicle (AV) that separate path planning and velocity planning and may use reference lines in combination with motion planners to determine paths. The method may include reference lines to project planning data into a S-L coordinate system. A motion planner algorithm uses the reference lines and previous path planning history to generate a path in the S-L coordinate system. A velocity is determined for the path. An AV controller is updated with the path and the velocity. Motion planning computations use a dynamic vehicle look-up table to determine possible vehicle motions based on an initial state and control input.

Motion planning methods and systems for autonomous vehicle

Disclosed herein are methods and systems for efficient optimal control with dynamic modeling for an autonomous vehicle (AV). The method may include acquiring vehicle status information for the AV, determining a longitudinal velocity of the AV, determining a driving style factor, wherein the driving style factor is dependent on at least road scenarios, obtaining an optimal control factor from a look-up table (LUT) using the determined longitudinal velocity and the determined driving style factor and providing an updated control command (such as a steering command) based on the obtained optimal control factor. The driving style factor may be determined from at least vehicle status, desired trajectory, current linear velocity and like parameters and ranges between a gentle driving mode and an aggressive driving mode.

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Westland, MI, United States of America

Duong Le