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arXiv · 2409.11694

From Words to Wheels: Automated Style-Customized Policy Generation for Autonomous Driving

Xu Han·Xianda Chen·Zhenghan Cai·Pinlong Cai·Meixin Zhu·Xiaowen Chu
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Abstract

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/.

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Xu Han, Xianda Chen, Zhenghan Cai, Pinlong Cai, Meixin Zhu, Xiaowen Chu. 2024-09-18. From Words to Wheels: Automated Style-Customized Policy Generation for Autonomous Driving. https://arxiv.org/abs/2409.11694

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