Conflict Resolution Model of Automated Vehicles Based on Multi-Vehicle Cooperative Optimization at Intersections

The traditional conflict resolution algorithm is designed for automated vehicles based on the premise of the right of way, but there is an unclear road right of way at unsignalized intersections, which brings trouble to the decision making of automated vehicles. The objective of this work is to provide our system with evolving cooperative and non-cooperative decisions. We achieve this by integrating game theory into the decision making. When the system decides to drive cooperatively, the joint action is planned to optimize the overall revenue of multi-vehicles based on the cooperative game, considering the conflict relationship with its neighboring vehicles. When the system fails to perform cooperative driving or response timeout, the vehicle-mounted unit would take non-cooperative driving to optimize the trajectory considering only its individual benefit. The proposed model can provide our system with stability and robustness, which effectively solved the conflict resolution problem when the right of way was not clear at intersections. We have implemented some simulation experiments of cooperative and non-cooperative conflict resolution, the results show that the revenue among various interest groups is more balanced with a cooperative conflict resolution method. Compared with the non-collaborative driving decision, the conflict resolution time is shortened, and the average delay of each vehicle at intersections is reduced by 1~2 s, with an average reduction of approximately 5%. The research can provide a reference for collaborative driving of automated vehicles at unsignalized intersections.