  Merging is, in general, a challenging task for both human drivers and
autonomous vehicles, especially in dense traffic, because the merging vehicle
typically needs to interact with other vehicles to identify or create a gap and
safely merge into. In this paper, we consider the problem of autonomous vehicle
control for forced merge scenarios. We propose a novel game-theoretic
controller, called the Leader-Follower Game Controller (LFGC), in which the
interactions between the autonomous ego vehicle and other vehicles with a
priori uncertain driving intentions is modeled as a partially observable
leader-follower game. The LFGC estimates the other vehicles' intentions online
based on observed trajectories, and then predicts their future trajectories and
plans the ego vehicle's own trajectory using Model Predictive Control (MPC) to
simultaneously achieve probabilistically guaranteed safety and merging
objectives. To verify the performance of LFGC, we test it in simulations and
with the NGSIM data, where the LFGC demonstrates a high success rate of 97.5%
in merging.
