Dynamic Local Path Planning for Intelligent Vehicles Based on Sampling Area Point Discrete and Quadratic Programming

This paper proposes a dynamic path planning method based on discrete optimization applied to suburban highways or expressways. We optimize the generated candidate points according to the cost function and then generates a local path through the quintic spline curve. The kinematics and obstacle boundary conditions are set up to improve the reliability of the planned path. Meanwhile, the two-dimensional normal distribution obstacle cost function, comfort cost function and acceleration cost function are designed to evaluate candidate points and speed. Various types of roads scenes, including dynamic and static obstacles, such as straight roads, S- bend, are established to verify the method’s feasibility. The simulation shows that the method can efficiently avoid various obstacles and plan an ideal path that complies with traffic laws.