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.
Graph-Based Adversarial Imitation Learning for Predicting Human Driving Behavior
2024-06-02
2626617 byte
Conference paper
Electronic Resource
English
Parallelized and Randomized Adversarial Imitation Learning for Safety-Critical Self-Driving Vehicles
ArXiv | 2021
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