Planning of High-Level Maneuver Sequences on Semantic State Spaces

Abstract

Highly automated driving is addressed more and more by research and also by vehicle manufacturers. In the past few years several demonstrations of automated vehicles driving on highways and even in urban scenarios were performed. In this context several challenges arose. One challenge is the understanding of complex situations and behavior generation within these especially in urban areas. Trajectory planning in these scenarios can be complex and expensive. Semantic scene modeling and planning can provide vital information to generate reliable and safe trajectories for automated vehicles. In this work we present a novel approach for high-level maneuver planning. It is based on a semantic state space that describes possible actions of a vehicle with respect to other scene elements like lane segments and traffic participants. The semantic characteristic of this state space allow for generalized planning even in complex situations. Concepts like heuristics and homotopies are utilized to optimize planning. Therefore, it is possible to efficiently generate high-level maneuver sequences for automated driving. The approach is tested on synthetic data as well as sensor data of a real test drive. and homotopies are utilized to optimize planning. Therefore, it is possible to efficiently generate high-level maneuver sequences for automated driving. The approach is tested on synthetic data as well as sensor data of a real test drive.