  We consider the problem of interaction-aware motion planning for automated
vehicles in general traffic situations. We model the interaction between the
controlled vehicle and surrounding road users using a generalized potential
game, in which each road user is assumed to minimize a common cost function
subject to shared (collision avoidance) constraints. We propose a quadratic
penalty method to deal with the shared constraints and solve the resulting
optimal control problem online using an Augmented Lagrangian method based on
PANOC. Secondly, we present a simple methodology for learning preferences and
constraints of other road users online, based on observed behavior. Through
extensive simulations in a highway merging scenario, we demonstrate the
practical efficacy of the overall approach as well as the benefits of the
proposed online learning scheme.
