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This paper studies game-theoretic decision-making for autonomous vehicles (AVs). A receding horizon multi-player game is formulated to model the AV decision-making problem. Two classes of games, including Nash game and Stackelber games, are developed respectively. For each of the two games, two solution settings, including pairwise games and multi-player games, are introduced, respectively, to solve the game in multi-agent scenarios. Comparative studies are conducted via statistical simulations to gain understandings of the performance of the two classes of games and of the two solution settings, respectively. The simulations are conducted in intersection-crossing scenarios, and the game performance is quantified by three metrics: safety, travel efficiency, and computational time., Comment: 8 pages, submitted to ECC24

This paper proposes a novel decision-making framework for autonomous vehicles (AVs), called predictor-corrector potential game (PCPG), composed of a Predictor and a Corrector. To enable human-like reasoning and characterize agent interactions, a receding-horizon multi-player game is formulated. To address the challenges caused by the complexity in solving a multi-player game and by the requirement of real-time operation, a potential game (PG) based decision-making framework is developed. In the PG Predictor, the agent cost functions are heuristically predefined. We acknowledge that the behaviors of other traffic agents, e.g., human-driven vehicles and pedestrians, may not necessarily be consistent with the predefined cost functions. To address this issue, a best response-based PG Corrector is designed. In the Corrector, the action deviation between the ego vehicle prediction and the surrounding agent actual behaviors are measured and are fed back to the ego vehicle decision-making, to correct the prediction errors caused by the inaccurate predefined cost functions and to improve the ego vehicle strategies. Distinguished from most existing game-theoretic approaches, this PCPG 1) deals with multi-player games and guarantees the existence of a pure-strategy Nash equilibrium (PSNE), convergence of the PSNE seeking algorithm, and global optimality of the derived PSNE when multiple PSNE exist; 2) is computationally scalable in a multi-agent scenario; 3) guarantees the ego vehicle safety under certain conditions; and 4) approximates the actual PSNE of the system despite the unknown cost functions of others. Comparative studies between the PG, the PCPG, and the control barrier function (CBF) based approaches are conducted in diverse traffic scenarios, including oncoming traffic scenario and multi-vehicle intersection-crossing scenario.

Decision-making for autonomous driving is challenging, considering the complex interactions among multiple traffic agents (e.g., autonomous vehicles (AVs), human drivers, 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. We show that the developed frameworks provide theoretical guarantees, including 1) existence of pure-strategy Nash equilibria, 2) convergence of the Nash equilibrium (NE) seeking algorithms, and 3) global optimality of the derived NE (in the sense that both self- and team- interests are optimized). In addition, we provide cost function shaping approaches to constructing multi-agent potential games in autonomous driving. Moreover, two solution algorithms, including self-play dynamics (e.g., best response dynamics) and potential function optimization, are developed for each game. The developed frameworks are then applied to two different traffic scenarios, including intersection-crossing and lane-changing in highways. Statistical comparative studies, including 1) finite potential game vs. continuous potential game, and 2) best response dynamics vs. potential function optimization, are conducted to compare the performances of different solution algorithms. It is shown that both developed frameworks are practical (i.e., computationally efficient), reliable (i.e., resulting in satisfying driving performances in diverse scenarios and situations), and robust (i.e., resulting in satisfying driving performances against uncertain behaviors of the surrounding vehicles) for real-time decision-making in autonomous driving.