A Comparison of Lateral Intention Models for Interaction-aware Motion Prediction at Highways

To safely navigate in complex scenarios is crucial to know the predictions of the vehicles involved in the scene. The future behavior of the traffic participants is dependent on their intentions, the road layout and the interaction between them. In this work, a framework is presented to compute the motion predictions of the surrounding vehicles considering all possible routes obtained from a given map. At each time step, with a Dynamic Bayesian Network, the probability of being on a specific route and the intention to change lanes are computed. Our framework, based on Markov chains, is generic and can handle various road layouts and any number of vehicles. We apply the framework in a two-lane highway and evaluate the influence of different lane-changing methods on the predictions of the vehicles present at the scene.