Toward safe and efficient autonomous platooning: A coalitional game-theoretic framework

Autonomous vehicle platooning in mixed-traffic environments faces critical safety challenges due to uncertain interactions with surrounding vehicles and the limitations of rigid platoon structures. In practice, unpredictable behaviors of human-driven vehicles can disrupt coordination and induce disturbances, while fixed platoon organizations lack the flexibility to adapt to evolving traffic conditions, leading to increased collision risk and degraded safety margins. To address this issue, this paper proposes a safety-oriented hierarchical framework for autonomous platooning, featuring a centralized decision-making and distributed execution architecture. At the upper level, a coalitional game-theoretic decision-making mechanism is developed to enable connected autonomous vehicles to form adaptive coalitions and generate interaction-aware behavioral strategies that explicitly consider both internal platoon safety and external traffic interactions. At the lower level, distributed motion controllers translate these strategies into dynamically feasible control actions, ensuring safe execution under vehicle constraints. The proposed framework is validated in representative mixed-traffic scenarios. Quantitative results demonstrate that the method significantly improves safety performance by increasing minimum time-to-collision and reducing exposure to critical risk conditions, while maintaining comparable operational efficiency. These findings confirm the effectiveness of the proposed framework in enabling safe and efficient autonomous platooning in complex traffic environments.