Probabilistic VRU Trajectory Forecasting for Model-Predictive Planning A Case Study: Overtaking Cyclists

Abstract

This article examines probabilistic trajectory forecasting methods of vulnerable road users (VRU) for the motion planning of autonomous vehicles. The future trajectories of a cyclist are predicted by Quantile Surface Neural Networks (QSN) and Mixture Density Neural Networks (MDN), both modeling confidence regions around the cyclist’s expected locations. Confidence regions are approximated by different methods with varying degrees of complexity to bridge the gap between forecasting and planning. Model-Predictive Planning (MPP) based on these regions is used for the autonomous vehicle. The approach is evaluated using a case study regarding safe trajectory planning for overtaking cyclists. The experiments show the effectiveness of the approach. Different considerations on the use of combined probabilistic trajectory prediction and vehicle trajectory planning are included.