Combining Model-Based Controllers and Generative Adversarial Imitation Learning for Traffic Simulation

作者：Haonan Chen, Tianchen Ji, Shuijing Liu, Katherine Driggs-Campbell · 发表于：2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) · 年份：2022 · DOI：10.1109/itsc55140.2022.9922261 · 被引用次数：5 · 研究领域：Autonomous Vehicle Technology and Safety、Traffic control and management、Traffic and Road Safety

An accurate model of human drivers is essential to validate the performance of autonomous vehicles in multiagent and interactive scenarios. Previous works on human driver modeling either use model-based controllers that are not adaptive and need laborious parameter-tuning or learn an end-to-end black box model that has few safety guarantees. We propose a two-stage hybrid driver model, where a high-level neural network generates driver traits that are used as the parameters of the low-level model-based controllers for simulated drivers. We train our model using generative adversarial imitation learning with reward augmentation and parameter sharing from real-world vehicle trajectory data. By combining data-driven and model-based approaches, our method simulates traffic agents with expressive, safe, and human-like behaviors. We demonstrate that our method outperforms state-of-the-art baselines in terms of imitation performance and safety in a multi-agent highway driving scenario.