Comparison of two Cooperative Maneuver Planning Approaches at a Real-World T-Junction

Connected automated driving promises a significant improvement of traffic efficiency and safety on highways and in urban areas. Cooperative maneuver planning may facilitate active guidance of connected automated vehicles at intersections. Research in automatic intersection management put forth a large body of works that mostly employ rule-based or optimization-based approaches primarily in fully automated simulated environments. In this work, we compare two cooperative planning approaches for unsignalized intersections that are capable of handling mixed traffic, i.e., the road being shared by automated vehicles and regular vehicles driven by humans. The first approach is a cooperative planner that selects the most efficient out of multiple possible maneuvers based on a scene prediction trained on real driving data. The second cooperative planning approach is based on graph-based reinforcement learning, which conquers the lack of ground truth data for cooperative maneuvers. We thoroughly evaluate both cooperative planners in a realistic high-fidelity simulation with fully automated traffic and mixed traffic. The simulative experiments show that cooperative maneuver planning leads to less delay due to interaction and a reduced number of stops. Furthermore, we present results from real-world experiments with three prototype automated vehicles at a T-junction in public traffic, in which both planning modules demonstrate their ability to perform efficient cooperative maneuvers.