A Modular and Model-Free Trajectory Planning Strategy for Automated Driving

Intelligent transportation systems that promise high level of automation, has gained significant attention during the last years. Movement planning of such vehicles is one of the most critical issues in order to realize autonomous driving. In this paper, a model-free trajectory planning concept that is based on the analytical solution of a given optimization problem is presented. In order to improve the longitudinal performance, an approach that is based on velocity profile partitioning is proposed. The proposed trajectory planning approach is implemented in a very modular way and tested in closed-loop software in the loop test environment. Effectiveness and capabilities of the proposed trajectory planning framework are validated over several case studies.