Summary of Driving with Regulation: Interpretable Decision-making For Autonomous Vehicles with Retrieval-augmented Reasoning Via Llm, by Tianhui Cai et al.
Driving with Regulation: Interpretable Decision-Making for Autonomous Vehicles with Retrieval-Augmented Reasoning via LLM
by Tianhui Cai, Yifan Liu, Zewei Zhou, Haoxuan Ma, Seth Z. Zhao, Zhiwen Wu, Jiaqi Ma
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 The proposed interpretable decision-making framework for autonomous vehicles integrates traffic regulations, norms, and safety guidelines comprehensively, enabling seamless adaptation to different regions. The framework consists of a Traffic Regulation Retrieval (TRR) Agent based on Retrieval-Augmented Generation (RAG), which automatically retrieves relevant traffic rules and guidelines from extensive regulation documents and records based on the ego vehicle’s situation. A reasoning module powered by a Large Language Model (LLM) interprets these rules, differentiates between mandatory rules and safety guidelines, and assesses actions on legal compliance and safety. The framework demonstrates robust performance on both hypothesized and real-world cases across diverse scenarios, with the ability to adapt to different regions with ease. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary For self-driving cars, this paper creates a system that helps them understand traffic rules and make good decisions. It uses special language models to read and understand lots of documents about traffic laws and regulations. The system can then figure out what’s allowed or not allowed in a specific situation. This makes the self-driving car more reliable and transparent. |
Keywords
» Artificial intelligence » Large language model » Rag » Retrieval augmented generation
