Summary of On-board Vision-language Models For Personalized Autonomous Vehicle Motion Control: System Design and Real-world Validation, by Can Cui et al.
On-Board Vision-Language Models for Personalized Autonomous Vehicle Motion Control: System Design and Real-World Validation
by Can Cui, Zichong Yang, Yupeng Zhou, Juntong Peng, Sung-Yeon Park, Cong Zhang, Yunsheng Ma, Xu Cao, Wenqian Ye, Yiheng Feng, Jitesh Panchal, Lingxi Li, Yaobin Chen, Ziran Wang
First submitted to arxiv on: 17 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel framework for personalized driving is proposed, leveraging Vision-Language Models (VLMs) to adapt autonomous vehicle behavior to individual users’ preferences while maintaining safety and comfort standards. The lightweight on-board framework integrates a Retrieval-Augmented Generation-based memory module for continuous learning through human feedback. Comprehensive real-world experiments demonstrate the system’s ability to provide safe, comfortable, and personalized driving experiences, reducing takeover rates by up to 76.9%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers develop an autonomous vehicle system that can learn individual drivers’ preferences and adapt its behavior accordingly. The system uses special computer models called Vision-Language Models (VLMs) to make decisions. This new approach is tested in real-world driving scenarios and shows promising results. |
Keywords
» Artificial intelligence » Retrieval augmented generation
