Graph-Based Adversarial Imitation Learning for Predicting Human Driving Behavior

Accurately predicting human driving behavior, particularly in highly interactive traffic scenarios, poses a significant challenge. In this work, we investigate the application of graph-based observations to Adversarial Imitation Learning (AIL) methods. Unlike conventional feature-based observations, this allows us to flexibly account for different road structures as well as a varying number of surrounding vehicles interacting with each other. We assess the method in a complex roundabout scenario from the INTERACTION dataset, employing several state-of-the-art AIL methods. The results indicate that our proposed approach successfully yields realistic driver models, applicable for accurate predictions of human driving behavior.