Potential Game-Based Decision-Making for Autonomous Driving

Decision-making for autonomous driving is challenging, considering the complex interactions among multiple traffic agents (including autonomous vehicles (AVs), human-driven vehicles, and pedestrians) and the computational load needed to evaluate these interactions. This paper develops two general potential game-based frameworks, namely, finite and continuous potential games, for decision-making in autonomous driving. The two frameworks account for the AVs’ two types of action spaces, i.e., finite and continuous action spaces, respectively. The developed frameworks provide theoretical guarantees for the existence of pure-strategy Nash equilibria and for the convergence of the Nash equilibrium (NE) seeking algorithms. The scalability challenge is also addressed. In addition, we provide cost function shaping approaches such that the agents’ cost functions not only reflect common driving objectives but also yield potential games. The performance of the developed algorithms is demonstrated in diverse traffic scenarios, including intersection-crossing and lane-changing scenarios. Statistical comparative studies, including 1) finite potential game vs. continuous potential game, 2) best response dynamics vs. potential function optimization, and 3) potential game vs. reinforcement learning (RL) vs. control barrier function (CBF), are conducted to compare the robustness against various surrounding vehicles’ strategies and to compare the computational efficiency. It is shown that the developed potential game frameworks have better robustness than RL and than CBF if the surrounding vehicles are not safety-conscious, and are computationally feasible for real-time implementation.