  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.
