Modeling pedestrian behavior in pedestrian-vehicle near misses: A continuous Gaussian Process Inverse Reinforcement Learning (GP-IRL) approach

Using simulation models to conduct safety assessments can have several advantages as it enables the evaluation of the safety of various design and traffic management options before actually making changes. However, limited studies have developed microsimulation models for the safety evaluation of active road users such as pedestrians. This can be attributed to the limited ability of simulation models to capture the heterogeneity in pedestrian behavior and their complex collision avoidance mechanisms. Therefore, the objective of this study is to develop an agent-based framework to realistically model pedestrian behavior in near misses and to improve the understanding of pedestrian evasive action mechanisms in interactions with vehicles. Pedestrian-vehicle conflicts are modeled using the Markov Decision Process (MDP) framework. A continuous Gaussian Process Inverse Reinforcement Learning (GP-IRL) approach is implemented to retrieve pedestrians' reward functions and infer their collision avoidance mechanisms in conflict situations. Video data from a congested intersection in Shanghai, China is used as a case study. Trajectories of pedestrians and vehicles involved in traffic conflicts were extracted with computer vision algorithms. A Deep Reinforcement Learning (DRL) model is used to estimate optimal pedestrian policies in traffic conflicts. Results show that the developed model predicted pedestrian trajectories and their evasive action mechanisms (i.e., swerving maneuver and speed changing) in conflict situations with high accuracy. As well, the model provided predictions of the post encroachment time (PET) conflict indicator that strongly correlated with the corresponding values of the field-measured conflicts. This study is a crucial step in developing a safety-oriented microsimulation tool for pedestrians in mixed traffic conditions.