US 11,731,612 B2
Neural network approach for parameter learning to speed up planning for complex driving scenarios
Jinyun Zhou, Sunnyvale, CA (US); Runxin He, Sunnyvale, CA (US); Qi Luo, Sunnyvale, CA (US); Jinghao Miao, Sunnyvale, CA (US); Jiangtao Hu, Sunnyvale, CA (US); Yu Wang, Sunnyvale, CA (US); Jiaxuan Xu, Sunnyvale, CA (US); and Shu Jiang, Sunnyvale, CA (US)
Assigned to BAIDU USA LLC, Sunnyvale, CA (US)
Filed by Baidu USA LLC, Sunnyvale, CA (US)
Filed on Apr. 30, 2019, as Appl. No. 16/399,538.
Prior Publication US 2020/0348676 A1, Nov. 5, 2020
Int. Cl. B60W 30/06 (2006.01); G05D 1/02 (2020.01); G06N 20/00 (2019.01); G06V 10/764 (2022.01); G06V 20/56 (2022.01); B60W 50/00 (2006.01); B60W 30/18 (2012.01)
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
OG exemplary drawing
 
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.