An Optimization-based Motion Planning Method for Autonomous Driving Vehicle

With the progress of autonomous driving technology, obtaining a safe and smooth trajectory in complex environments is the focus of motion planning in recent years. This paper presents a post-optimization method based on the gradient descent and Bèzier curve, which can obtain a safer and more comfortable trajectory in the static obstacle scene. The optimized path is farther away from obstacles locally and can be tracked by the low-controller. Our post-optimization method has two main steps which consider the factors of collision avoidance and smoothing separately. The first step uses the gradient descent method to optimize so that the path is farther away from obstacles. The cost function of the gradient descent method mainly considers obstacle punishment. The second step uses the variable order Bèzier curve to smooth segments which have sharp inflection points caused by the gradient descent. The simulation experiments have been performed to evaluate the feasibility and efficiency of the proposed method. The simulation results show that our algorithm can achieve the purpose of staying away from obstacles locally and refine the vehicle smoothness as well.