Co-design of Reachability Analysis and Trajectory Planning for Collision Avoidance Systems

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Collision avoidance systems for road vehicles potentially taking over the full control have to face many challenges. Among them are uncertain measurements of the environment, uncertain future movements of other traffic participants, and the often small solution space for a safe motion. The small solution space is intentional since collision avoidance systems should only engage when a driver has almost no possibility left to bring the vehicle into a safe state. While motion planning can be considered as rather well researched, the situation is completely different in emergency situations: The computation time of state-of-the-art motion planners is the larger, the smaller the solution space is. This contradicts the need of small computation times in critical situations, thus safe solutions are often not found and a crash is inevitable. In contrast, reachability analysis becomes the faster, the smaller the solution space is (reachability analysis returns the set of possible solutions for a dynamical system). In this project, we develop a novel co-design of reachability analysis and motion planning to realize a motion planner with small computation times in dangerous situations. By using reachable sets, one can better prune the search space of graph-based planners and better guide planners using gradient-based continuous optimization. We will also identify narrow passages using reachable sets to make sure that the motion planner passes those without causing any collisions. Further, we automatically derive safe states in which a vehicle can stay indefinitely without causing a collision. This makes it possible to provide safe motion plans for infinite time horizons. To further save computation time, we aim at repairing unsafe motion plans, i.e., only change critical parts so that only collision checks are required to be re-run for the repaired part.The proposed concept will be intensively tested by automatically-generated, critical situations (and their evolvement). The automatic test generation will also be implemented on a server so that other researchers can test their motion planners as well. This would make it possible for the first time to benchmark other approaches since no standardized tests yet exist for automated road vehicles. The obtained results can also be used for automated driving to guarantee safe solutions in critical situations. Also other new intelligent systems, which also have to guarantee a safe operation, such as partially automated medical robots, systems realizing safe human-robot co-existence in production, as well as smart grids, benefit from our results.