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To safely navigate in complex scenarios is crucial to know th e predictions of the vehicles involved in the scene. The future behavior of the traffic participants is dep endent on their intentions, the road layout and the interaction between them. In this work, a framework is prese nted to compute the motion predictions of the surrounding vehicles considering all possible routes obta ined from a given map. At each time step, with a Dynamic Bayesian Network, the probability of being on a spec ific 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 high way and evaluate the influence of different lane-changing methods on the predictions of the vehicles pr ent at the scene.

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