A comparison of motion planning algorithms for cooperative collision avoidance of multiple cognitive automobiles

Automated cooperative collision avoidance of multiple vehicles is a promising approach to increase road safety in the future. This approach requires a real-time motion planner which computes cooperative maneuvers of multiple cognitive vehicles. As motion planning is a task of high computational complexity, computing times of the planner have to be traded off against solution quality. This contribution compares several cooperative motion planning algorithms with respect to these criteria. The considered algorithms are a tree search algorithm relying on precomputed lower bounds, the elastic band method, mixed-integer linear programming, and a priority-based approach. Success rates and computing times on various simulated scenarios are reported.