A Homogeneous Multi-Vehicle Cooperative Group Decision-Making Method for Complicated Mixed Traffic Scenarios

Abstract

Connected and Automated Vehicles (CAVs) is expected to reshape the transportation system, and cooperative group intelligence of CAVs has great potential for improving transportation efficiency and safety. One challenge for CAV group driving is the decision-making under scenarios mixed with CAV and human-driven vehicles (HDV). Current studies mainly use methods based on single physical rules such as platoon driving or formation switch control, failing to reach a balanced and homogeneous state of optimal efficiency and risk. In addition, most studies focus only on one specific type of scene, lacking the generalization ability to adapt to various surrounding conditions. This paper proposes a homogeneous multi-vehicle cooperative group decision-making method targeting mixed traffic scenarios. A bi-level framework composed of behavior-level and trajectory-level decision making is established to achieve balanced optimal cooperation. A region-driven behavioral decision mechanism is designed to decompose vehicle actions into a unified form of sequential target regions. Solutions are derived based on Cooperative Driving Safety Field, a risk assessment module inspired by field energy theory. The trajectory-level decision module takes the target regions as input and generates the control quantities of the CAVs through target point selection, conflict reconciliation, and dynamic constraint consideration. Experimental results on 19 various scenarios indicate that the proposed method significantly increases the passing efficiency, reduces the driving risk, and improves the generalization ability. Feasibility is also verified through physical platform validations.

Keywords: Autonomous Driving, Connected and automated vehicles, Decision-making, Risk assessment