Cooperative Multi-Vehicle Behavior Coordination for Autonomous Driving

Creating rational driving options and designing the decision process to select the best solution in a traffic situation with multiple participants present is a challenging problem. Other participants could be cooperating communication-enabled autonomous vehicles or vehicles controlled by human drivers with egoistic goals. This work introduces a novel approach to coordinate the behavior of multiple vehicles in generic traffic scenes. Our three-step method generates motion options neglecting vehicle interactions at first. Afterward, a mixed-integer linear optimization problem is solved to find the optimally coordinated motion patterns, followed by an online re-calibration based on the observed behaviors in reality. We demonstrate and evaluate the applicability in an evasive maneuver requiring vehicle interaction in detail and also present an intersection scenario. We further show that cooperative behavior, as well as egoistic driver intentions, can be handled safely and analyze the properties of the proposed solution.