CPC B60W 30/06 (2013.01) [G05D 1/0221 (2013.01); G05D 1/0246 (2013.01); G05D 1/0274 (2013.01); G06N 20/00 (2019.01); G06V 10/764 (2022.01); G06V 20/56 (2022.01); G06V 20/588 (2022.01); G05D 2201/0213 (2013.01)] | 21 Claims |
1. A computer-implemented method of operating an autonomous driving vehicle (ADV), comprising:
perceiving, by a processor, a driving environment surrounding the ADV based on sensor data obtained from one or more sensors mounted on the ADV;
determining, by the processor, a driving scenario, in response to a driving decision based on the driving environment;
applying, by the processor, a predetermined machine-learning model to data representing the driving environment and the driving scenario to generate a set of driving parameters, wherein the set of driving parameters include a speed, an acceleration, an acceleration penalty, a curvature, a curvature penalty, and a heading direction, and wherein the acceleration penalty and the curvature penalty are coefficients of a cost function to determine an optimal acceleration penalty and an optimal curvature penalty;
determining the cost function=(Pa×α)+(Pk×κ), wherein Pa is the acceleration penalty, α (alpha) is the acceleration, Pk is the curvature penalty, κ (kappa) is the curvature, and Pa and Pk are coefficients of the cost function, and wherein when the driving scenario is a U-turn driving scenario, the optimal Pa is equal to 0.7, and the optimal Pk is equal to 0.3;
planning, by the processor, a trajectory to navigate the ADV using the set of the driving parameters according to the driving scenario through the driving environment based on the cost function; and
controlling, by the processor, the ADV to navigate according to the trajectory.
|