Pedestrian Flow Estimation Using Sparse Observation for Autonomous Vehicles

One of the major challenges that autonomous cars are facing today is the unpredictability of pedestrian movement in urban environments. Since pedestrian data acquired by vehicles are sparse observed a pedestrian flow directed graph is proposed to understand pedestrian behavior. In this work, an autonomous electric vehicle is employed to gather LiDAR and camera data. Pedestrian tracking information and semantic information from the environment are used with a probabilistic approach to create the graph. In order to refine the graph a set of outlier removal techniques are described. The graph-based pedestrian flow shows an increase of 61.29% of coverage zone, and the outlier removal approach successfully removed 81% of the edges.