  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.
