Decoupled Sampling-Based Velocity Tuning and Motion Planning Method for Multiple Autonomous Vehicles

This paper describes a decoupled sampling-based motion-planning method, based on the rapidly-exploring random tree (RRT) approach, that is applicable to autonomous vehicles, in order to perform different traffic maneuvers. This is a two-step motion-planning method including path-planning and motion timing steps, where both steps are sampling-based. In the path-planning part, an improved RRT method is defined that increases the smoothness of the path and decreases the computational time of the RRT method; it is called smooth RRT, SRRT. While some other RRT-based methods such as RRT can perform better in winding roads, in the problem of interest in this paper (which is performing some regular traffic maneuvers in usual urban roads and highways where the passage is not too winding), SRRT is more efficient since the computational time is less than for the other considered methods. In the motion timing or velocity-tuning step (VTS), a sampling-based method is introduced that guarantees collision avoidance between different vehicles. The proposed motion-timing algorithm can be very useful for collision avoidance and can be used with any other path-planning method. Simulation results show that because of the probabilistic property of the SRRT and VTS algorithms, together with the decoupling feature of the method, the algorithm works well for different traffic maneuvers.