  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.
