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.


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    Title :

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


    Contributors:


    Publication date :

    2022-10-08


    Size :

    626176 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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