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

Understand

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.