Summary of Vlm-ad: End-to-end Autonomous Driving Through Vision-language Model Supervision, by Yi Xu et al.
VLM-AD: End-to-End Autonomous Driving through Vision-Language Model Supervision
by Yi Xu, Yuxin Hu, Zaiwei Zhang, Gregory P. Meyer, Siva Karthik Mustikovela, Siddhartha Srinivasa, Eric M. Wolff, Xin Huang
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 This paper proposes VLM-AD, a method that leverages vision-language models (VLMs) to enhance training for autonomous driving (AD) tasks. The existing E2E AD models are optimized to mimic driving patterns observed in data but lack the underlying reasoning processes, limiting their ability to handle challenging scenarios. VLM-AD incorporates unstructured reasoning information and structured action labels as additional supervision, allowing the model to learn richer feature representations that capture the rationale behind driving patterns. This approach does not require a VLM during inference, making it practical for real-time deployment. When integrated with state-of-the-art methods, VLM-AD achieves significant improvements in planning accuracy and reduced collision rates on the nuScenes dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VLM-AD is a new way to train autonomous driving models that helps them make better decisions by using common sense and reasoning. Right now, most AD models just copy what they’ve learned from data without understanding why they’re making those choices. This makes it hard for them to handle tricky situations. The VLM-AD method uses special language models as a teacher to provide more guidance during training. This helps the model learn more about the underlying reasons behind driving patterns, which leads to better planning and fewer accidents. |
Keywords
» Artificial intelligence » Inference
