Bringing Diversity to Autonomous Vehicles: An Interpretable Multi-vehicle Decision-making and Planning Framework

With the development of autonomous driving, it is becoming increasingly common for autonomous vehicles (AVs) and humandriven vehicles (HVs) to share the same roads. We propose a hierarchical multi-vehicle decision-making and planning framework with several advantages. The framework makes decisions jointly for all vehicles within the traffic flow and reacts promptly to the dynamic environment through a high-frequency planning module. The decision module produces interpretable action sequences that can explicitly communicate self-intentions to the surrounding HVs. We also present the cooperation factor and the trajectory weight set, which bring diversity to autonomous vehicles in traffic at both the social and individual levels.