A data-driven approach for pedestrian intention estimation

In the context of future urban automated driving many important problems remain unsolved. A critical one is the analysis and prediction of pedestrian movements around urban roads. Especially the analysis of non-critical situations has not received much attention in the past. This paper focuses on analyzing and predicting movements of pedestrians approaching crosswalks, a very crucial pedestrian-vehicle interaction in urban scenarios. In our previous work, we analyzed the performance of a data-driven Support Vector Machine-based architecture, and the relevance of specific features to infer pedestrian crossing intentions. In this paper, we will use our previous results as baseline to compare against an architecture based on neural networks for time-series classification. In particular we analyze the effectiveness of dense and Long-Short-Term-Memory networks. Furthermore, we will be looking into enhancing our feature vectors by adding LiDAR based images to the classification process. Additionally the evaluation provides an estimate for the temporal prediction horizon. The approaches presented are validated with real world trajectories recorded in Germany. Our results show an average accuracy improvement of 10–20% with respect to our previous Support Vector Machine-based approach.