Road infrastructure indicators for trajectory prediction

Safe and comfortable path planning in a dynamic urban environment is essential to an autonomous vehicle. This requires the future trajectories of all other road users in the environment of the vehicle. These trajectories are predicted through modeling the motion and behaviour of these road users. In this work we state that for efficient trajectory prediction only motion indicators are not sufficient. Therefore, we propose using a curvilinear coordinate system with curvature as road infrastructure indicators to improve motion modeling and trajectory prediction. With experiments, we show that the curvilinear coordinate system with curvature sufficiently incorporates the road structure. Furthermore, we show that a sequence-tosequence RNN model is suitable to incorporate road curvature indicators directly into the modeling and prediction.