From Words to Wheels: Automated Style-Customized Policy Generation for Autonomous Driving
<|reference_start|>From Words to Wheels: Automated Style-Customized Policy Generation for Autonomous Driving: Autonomous driving technology has witnessed rapid advancements, with foundation models improving interactivity and user experiences. However, current autonomous vehicles (AVs) face significant limitations in delivering command-based driving styles. Most existing methods either rely on predefined driving styles that require expert input or use data-driven techniques like Inverse Reinforcement Learning to extract styles from driving data. These approaches, though effective in some cases, face challenges: difficulty obtaining specific driving data for style matching (e.g., in Robotaxis), inability to align driving style metrics with user preferences, and limitations to pre-existing styles, restricting customization and generalization to new commands. This paper introduces Words2Wheels, a framework that automatically generates customized driving policies based on natural language user commands. Words2Wheels employs a Style-Customized Reward Function to generate a Style-Customized Driving Policy without relying on prior driving data. By leveraging large language models and a Driving Style Database, the framework efficiently retrieves, adapts, and generalizes driving styles. A Statistical Evaluation module ensures alignment with user preferences. Experimental results demonstrate that Words2Wheels outperforms existing methods in accuracy, generalization, and adaptability, offering a novel solution for customized AV driving behavior. Code and demo available at https://yokhon.github.io/Words2Wheels/.<|reference_end|>
@article{han2024from,
    title={From Words to Wheels: Automated Style-Customized Policy Generation for
  Autonomous Driving},
    author={Xu Han, Xianda Chen, Zhenghan Cai, Pinlong Cai, Meixin Zhu, Xiaowen
  Chu},
    journal={arXiv preprint arXiv:2409.11694},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.11694},
    primaryClass={cs.RO}
}