In this paper we treat optimal trajectory planning for an autonomous vehicle
(AV) operating in dense traffic, where vehicles closely interact with each
other. To tackle this problem, we present a novel framework that couples
trajectory prediction and planning in multi-agent environments, using
distributed model predictive control. A demonstration of our framework is
presented in simulation, employing a trajectory planner using non-linear model
predictive control. We analyze performance and convergence of our framework,
subject to different prediction errors. The results indicate that the obtained
locally optimal solutions are improved, compared with decoupled prediction and
planning