Summary of Rac3: Retrieval-augmented Corner Case Comprehension For Autonomous Driving with Vision-language Models, by Yujin Wang et al.
RAC3: Retrieval-Augmented Corner Case Comprehension for Autonomous Driving with Vision-Language Models
by Yujin Wang, Quanfeng Liu, Jiaqi Fan, Jinlong Hong, Hongqing Chu, Mengjian Tian, Bingzhao Gao, Hong Chen
First submitted to arxiv on: 15 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 called RAC3 is proposed to improve Vision-Language Models’ (VLMs) ability to handle corner cases effectively in autonomous driving systems. The framework integrates Retrieval-Augmented Generation (RAG) to mitigate hallucination by incorporating context-specific external knowledge, and cross-modal alignment fine-tuning utilizing contrastive learning for robust retrieval of similar scenarios. Extensive experiments on a curated dataset demonstrate RAC3’s ability to enhance semantic alignment, improve hallucination mitigation, and achieve superior performance metrics such as Cosine Similarity and ROUGE-L scores. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RAC3 is a new way to help computers understand complex driving situations better. It uses special models called Vision-Language Models (VLMs) that can look at pictures and understand what they mean. But sometimes these models make mistakes, like imagining things that aren’t really there. RAC3 helps fix this problem by adding more information from the internet and making sure the model is looking at things in a way that makes sense. This means that computers can better understand and predict complex driving situations. |
Keywords
» Artificial intelligence » Alignment » Cosine similarity » Fine tuning » Hallucination » Rag » Retrieval augmented generation » Rouge
