Path and velocity trajectory selection in an anticipative kinodynamic motion planner for autonomous driving

Abstract

This paper presents an approach for plan generation, seletion and pruning of trajectories for autonomous driving, capable of dealing with dynamic complex environments, such as driving in urban scenarios. The planner rst discretizes the plan space and searches for the best trajectory and velocity prole of the vehicle. The main contributions of this work are the use of G2-splines for path generation and a method that takes into account accelerations and passenger comfort for generating and pruning velocity profiles based on 3rd order splines, both fulfilling kino-dynamic constraints. The proposed methods have been implemented in a motion planner in MATLAB and tested through simulation in different representative scenarios, involving obstacles and other moving vehicles. The simulations show that the planner performs correctly in different dynamic scenarios, maintaining the passenger comfort.