An integrated game-theoretic framework for lane changes of a single connected and autonomous vehicle in human-driven traffic

Lane changing is a crucial maneuver for connected and autonomous vehicles (CAV) to achieve efficient and safe driving, demanding tightly coupled decision-making and trajectory planning supported by accurate predictions of surrounding traffic. We introduce an integrated architecture for CAV coupling three core functionalities: predictive modeling of adjacent vehicle trajectories, game-theoretic multi-agent decision strategies, and adaptive real-time path generation. The framework comprises three core modules: First, a learning-based model estimates surrounding vehicles’ motions. Second, a multi-vehicle game-theoretic decision-making module evaluates various lane change scenarios, such as successful lane change, failed lane-changing attempts, and re-lane change after failed lane-changing attempts. Third, a quintic-poluomial planner continuously plans the CAV’s trajectory in real time. We conduct joint simulation experiments for specific lane changing scenarios, in which CAV strictly implements framework functions and evaluate three performance dimensions: forecasting precision, strategic decision efficacy, and trajectory optimization metrics. In mandatory lane change scenarios, the proposed framework achieved an 80 % success rate, with the full process from intention initiation to successful lane-change completion averaging 4.0 s, outperforming benchmark models. In discretionary scenarios, it reached a 70 % success rate and a 6.8-second completion time, compared to 30 % and 9.0 s for existing methods. These results demonstrate that integrating motion prediction, game-theoretic decision making, and adaptive planning enhances both the safety and efficiency of CAV lane change behavior.