Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Methods and apparatus are described for motion planning of a vehicle. A motion planner uses previous motion graph data included in a motion graph tree and a look-up table (LUT) to generate candidate trajectory(ies). The motion graph tree is updated with motion graph data associated with the candidate trajectory(ies) upon a terminate condition. A trajectory from the candidate trajectory(ies) is selected and a controller is updated with the trajectory to control the vehicle. A path planner uses previous configuration graph data in a configuration graph tree to generate candidate path(s). The configuration graph tree is updated with configuration graph data associated with the candidate path(s) upon a terminate conditions. A path is selected from the candidate path(s). A velocity planner algorithm determines a velocity from the path. A LUT is used to assist in the velocity determination. A controller is updated with the path and velocity to control the vehicle.

Abstract: 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.