Potential-Game Structured Cooperative Eco-Driving for Mixed Platoons at Signalized Intersections

In modern transportation systems, eco-driving aims to reduce fuel consumption and emissions while maintaining traffic efficiency and safety. Existing eco-driving methods at signalized intersections often rely on accurately prescribed arrival times, which are difficult to obtain in mixed traffic due to the motion uncertainty of human-driven vehicles (HVs) and the variability of traffic-light phases. Moreover, the interaction between connected and automated vehicles (CAVs) and surrounding HVs is often insufficiently modeled in existing approaches. To address these challenges, this paper proposes a potential-game-based eco-driving framework for mixed platoons at signalized intersections. The proposed method employs an artificial potential field (APF) within a recedinghorizon control architecture to optimize the trajectories of CAVs online, while incorporating predicted HV motion as interactive information. The resulting multi-CAV coordination problem is reformulated as an exact potential game, for which the existence of a pure-strategy Nash equilibrium is established, and local minimizers of the induced potential function correspond to local Nash equilibria. Extensive simulation results demonstrate that the proposed framework effectively reduces delay, idling time, and fuel consumption, while enabling smooth and adaptive intersection crossing under dynamic traffic-light conditions.