Goal-Directed Pedestrian Prediction

Recent advances in road safety have lead to a constant decline of injured traffic participants in Europe per year. Still, the number of injured pedestrians remains nearly constant. As a countermeasure, active pedestrian safety is the focus of current research, for which accurate pedestrian prediction is a prerequisite. In this scope, we propose a method for dynamics-and environment-based pedestrian prediction. We introduce the pedestrian's destination as a latent variable and thus convert the prediction problem into a planning problem. The planning is executed based on the current dynamics of the pedestrian. The distribution over the destinations is modeled using a Particle Filter. Experimental results show a significant improvement over existing approaches such as Kalman Filters.