  Understanding the intention of vehicles in the surrounding traffic is crucial
for an autonomous vehicle to successfully accomplish its driving tasks in
complex traffic scenarios such as highway forced merging. In this paper, we
consider a behavioral model that incorporates both social behaviors and
personal objectives of the interacting drivers. Leveraging this model, we
develop a receding-horizon control-based decision-making strategy, that
estimates online the other drivers' intentions using Bayesian filtering and
incorporates predictions of nearby vehicles' behaviors under uncertain
intentions. The effectiveness of the proposed decision-making strategy is
demonstrated and evaluated based on simulation studies in comparison with a
game theoretic controller and a real-world traffic dataset.
