Interaction aware cooperative trajectory planning for lane change maneuvers in dense traffic

In order to generate favorable trajectories, road users need to cope with interaction among them, especially in dense traffic. Thus, for autonomous cars, the intention of involved vehicles needs to be considered in their motion planning. This paper proposes a general framework for cooperative interaction aware trajectory generation based on multiagent trajectory planning. Possible intentions are distinguished by different cost functions, resulting in different behaviors such as cooperative or non-cooperative. Given observations, Bayesian estimation is used to obtain a probability distribution of the intention models. Considering these probabilities during prediction and planning results in trajectories taking uncertain interaction with surrounding vehicles into account. The performance of the approach is demonstrated via numerical experiments for a lane change scenario in dense traffic.