Search-Based Optimal Motion Planning for Automated Driving

• Published in: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) pp. 4523 - 4530
• Main Authors: Ajanovic, Zlatan, Lacevic, Bakir, Shyrokau, Barys, Stolz, Michael, Horn, Martin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2018
• Subjects: A search; automated driving; Automation; Dynamics; lane change; motion planning; MPC; multi-lane driving; Planning; Roads; Search problems; traffic lights; Trajectory; Vehicle dynamics
• ISSN: 2153-0866
• DOI: 10.1109/IROS.2018.8593813

This paper presents a framework for fast and robust motion planning designed to facilitate automated driving. The framework allows for real-time computation even for horizons of several hundred meters and thus enabling automated driving in urban conditions. This is achieved through several features. Firstly, a convenient geometrical representation of both the search space and driving constraints enables the use of classical path planning approach. Thus, a wide variety of constraints can be tackled simultaneously (other vehicles, traffic lights, etc.). Secondly, an exact cost-to-go map, obtained by solving a relaxed problem, is then used by A*-based algorithm with model predictive flavour in order to compute the optimal motion trajectory. The algorithm takes into account both distance and time horizons. The approach is validated within a simulation study with realistic traffic scenarios. We demonstrate the capability of the algorithm to devise plans both in fast and slow driving conditions, even when full stop is required.