  Planning under social interactions with other agents is an essential problem
for autonomous driving. As the actions of the autonomous vehicle in the
interactions affect and are also affected by other agents, autonomous vehicles
need to efficiently infer the reaction of the other agents. Most existing
approaches formulate the problem as a generalized Nash equilibrium problem
solved by optimization-based methods. However, they demand too much
computational resource and easily fall into the local minimum due to the
non-convexity. Monte Carlo Tree Search (MCTS) successfully tackles such issues
in game-theoretic problems. However, as the interaction game tree grows
exponentially, the general MCTS still requires a huge amount of iterations to
reach the optima. In this paper, we introduce an efficient game-theoretic
trajectory planning algorithm based on general MCTS by incorporating a
prediction algorithm as a heuristic. On top of it, a social-compliant reward
and a Bayesian inference algorithm are designed to generate diverse driving
behaviors and identify the other driver's driving preference. Results
demonstrate the effectiveness of the proposed framework with datasets
containing naturalistic driving behavior in highly interactive scenarios.
