Trajectory tracking and prediction of pedestrian's crossing intention using roadside LiDAR

Abstract

Trajectory tracking and crossing intention prediction of pedestrians at intersections are important to intersection safety. Recently, on-board video sensors have been developed for detection of pedestrians. However, both the detection range and operating environment of video-based systems seem to be constrained by the advancement of image-processing technologies. Additionally, on-board systems cannot alarm pedestrians to take evasive actions when at risk, a feature which is critical to saving lives. This paper summarises the authors' practice on using roadside LiDAR sensors to monitor and predict pedestrians' crossing intention, as part of an ongoing effort to develop a pioneering LiDAR-based system to systematically reduce pedestrian and vehicle collisions at intersections. The LiDAR sensors were installed at intersections to collect pedestrian data such as presence, location, velocity, and direction. A new method based on deep autoencoder - artificial neural network (DA-ANN) was used to process data and predict pedestrian crossing intention. The case study shows the proposed model is about 95% prediction accuracy and computational efficiency for real-time systems. The roadside LiDAR system has great potential to significantly reduce vehicle-to-pedestrian crashes both at intersections and non-intersection areas, either used as a stand-alone system or in conjunction with the connected V2I and I2V technologies.