CHARMS: Cognitive Hierarchical Agent with Reasoning and Motion Styles

To address the current challenges of low intelligence and simplistic vehicle behavior modeling in autonomous driving simulation scenarios, this paper proposes the Cognitive Hierarchical Agent with Reasoning and Motion Styles (CHARMS). The model can reason about the behavior of other vehicles like a human driver and respond with different decision-making styles, thereby improving the intelligence and diversity of the surrounding vehicles in the driving scenario. By introducing the Level-k behavioral game theory, the paper models the decision-making process of human drivers and employs deep reinforcement learning to train the models with diverse decision styles, simulating different reasoning approaches and behavioral characteristics. Building on the Poisson cognitive hierarchy theory, this paper also presents a novel driving scenario generation method. The method controls the proportion of vehicles with different driving styles in the scenario using Poisson and binomial distributions, thus generating controllable and diverse driving environments. Experimental results demonstrate that CHARMS not only exhibits superior decision-making capabilities as ego vehicles, but also generates more complex and diverse driving scenarios as surrounding vehicles. We will release code for CHARMS atthis https URL.

@article{wang2025_2504.02450,
  title={ CHARMS: Cognitive Hierarchical Agent with Reasoning and Motion Styles },
  author={ Jingyi Wang and Duanfeng Chu and Zejian Deng and Liping Lu },
  journal={arXiv preprint arXiv:2504.02450},
  year={ 2025 }
}