Deep learning-based pedestrian trajectory prediction and risk assessment at signalized intersections using trajectory data captured through roadside LiDAR

AbstractAbstractIn recent years, rapid advancements in the Autonomous Vehicles (AVs) industry have greatly motivated the research and development in pedestrian trajectory prediction and risk assessment. One of the critical requirements for AVs is to predict the future trajectories of pedestrians and provide collision warnings in an accurate and prompt manner. Nevertheless, accurate prediction of pedestrian trajectories remains a technical challenge, mainly caused by the heterogeneity of pedestrian crossing behavior and uncertainties in vehicle-pedestrian interactions. This paper proposes a deep learning-based method for pedestrian trajectory prediction and risk assessment, using trajectory data extracted from roadside LiDAR data and corresponding signal phasing information at MLK and Georgia Avenue in Chattanooga, TN. Meanwhile, a set of criteria referred to as the risk factor is established to quantitatively evaluate the risk of the pedestrian crossing behavior, which also serves as a learnable feature. A Long Short-Term Memory (LSTM) network is proposed, which takes the following data as the input: the pedestrian trajectory data, signal phasing data, and risk factors from the past 10 steps. Meanwhile, the network predicts the pedestrian trajectory and risk factor at the future time step. In the experimental study, the root-mean-square errors between the predicted and ground truth x and y coordinates are 0.225 meters and 0.377 meters, respectively, and the F1 score value for the risk factor is 99.6%, demonstrating the efficacy of the proposed LSTM-based methodology on pedestrian trajectory prediction and risk assessment.Keywords: Deep learningLong Short-Term Memory (LSTM) networkpedestrian trajectory predictionrisk assessmentroadside LiDAR datavulnerable road user (VRU) AcknowledgmentsThe authors would like to thank the Transportation Research Board (TRB) committee AED50: Artificial Intelligence and Advanced Computing Applications for organizing the 2022 Transportation Forecasting (TRANSFOR 22) Competition. The authors would also like to thank the Center for Urban Informatics and Progress (CUIP) at The University of Tennessee at Chattanooga (UTC), National Science Foundation (NSF), City of Chattanooga, Ouster LiDAR, and Seoul Robotics for sponsoring the competition and providing the data.Disclosure statementNo potential conflict of interest was reported by the author(s).