Imitating Driver Behavior with Generative Adversarial Networks
This work addresses the need for accurate driver behavior simulation for intelligent transportation systems, representing an incremental improvement over prior methods.
The paper tackled the problem of predicting and simulating human driving behavior by extending Generative Adversarial Imitation Learning to train recurrent policies, resulting in a model that outperforms rule-based controllers and maximum likelihood models in realistic highway simulations.
The ability to accurately predict and simulate human driving behavior is critical for the development of intelligent transportation systems. Traditional modeling methods have employed simple parametric models and behavioral cloning. This paper adopts a method for overcoming the problem of cascading errors inherent in prior approaches, resulting in realistic behavior that is robust to trajectory perturbations. We extend Generative Adversarial Imitation Learning to the training of recurrent policies, and we demonstrate that our model outperforms rule-based controllers and maximum likelihood models in realistic highway simulations. Our model both reproduces emergent behavior of human drivers, such as lane change rate, while maintaining realistic control over long time horizons.