# Driving with Regulation: Trustworthy and Interpretable Decision-Making for Autonomous Driving with Retrieval-Augmented Reasoning

arXiv: https://arxiv.org/abs/2410.04759 · HTML: https://mlsanity.com/paper/2410.04759/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Placed in Chain-of-thought reasoning for driving, one of 17 directions in LLM reasoning.
- Builds on 13 of its 46 references that are in the map.
- First posted on arXiv 7 Oct 2024.

## Placed in

- LLM reasoning / [Chain-of-thought reasoning for driving](https://mlsanity.com/lr/chain-of-thought-reasoning-for-driving/)
- Retrieval-augmented generation / [Multimodal RAG](https://mlsanity.com/rag/multimodal-rag/)
- End-to-end autonomous driving / [Large language models for driving](https://mlsanity.com/driving/large-language-models-for-driving/)

## Builds on (most cited first, 13 of 13)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [WebGPT: Browser-assisted question-answering with human feedback](https://mlsanity.com/paper/2112.09332/) (2021)
- [Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting](https://mlsanity.com/paper/2301.00493/) (2023)
- [DriveLM: Driving with Graph Visual Question Answering](https://mlsanity.com/paper/2312.14150/) (2023)
- [ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst](https://mlsanity.com/paper/1812.03079/) (2018)
- [GPT-Driver: Learning to Drive with GPT](https://mlsanity.com/paper/2310.01415/) (2023)
- [A Language Agent for Autonomous Driving](https://mlsanity.com/paper/2311.10813/) (2023)
- [Large Motion Model for Unified Multi-Modal Motion Generation](https://mlsanity.com/paper/2404.01284/) (2024)
- [Driving Everywhere with Large Language Model Policy Adaptation](https://mlsanity.com/paper/2402.05932/) (2024)
- [Towards Knowledge-driven Autonomous Driving](https://mlsanity.com/paper/2312.04316/) (2023)
- [Socially-Aware Robot Navigation Enhanced by Bidirectional Natural Language Conversations Using Large Language Models](https://mlsanity.com/paper/2409.04965/) (2024)

## Built on by (most cited first, 13 of 13)

- [Cognitive Edge Computing: A Comprehensive Survey on Optimizing Large Models and AI Agents for Pervasive Deployment](https://mlsanity.com/paper/2501.03265/) (2025)
- [nuReasoning: A Reasoning-Centric Dataset and Benchmark for Long-Tail Autonomous Driving](https://mlsanity.com/paper/2605.31572/) (2026)
- [AgentDrive: An Open Benchmark Dataset for Agentic AI Reasoning with LLM-Generated Scenarios in Autonomous Systems](https://mlsanity.com/paper/2601.16964/) (2026)
- [Case-based Reasoning Augmented Large Language Model Framework for Decision Making in Realistic Safety-Critical Driving Scenarios](https://mlsanity.com/paper/2506.20531/) (2025)
- [From Prompts to Pavement Through Time: Temporal Grounding in Agentic Scene-to-Plan Reasoning](https://mlsanity.com/paper/2605.19824/) (2026)
- [Large Language Models in Transportation Systems Management and Operations: From Text Reasoning to Multi-modal Decision Support](https://mlsanity.com/paper/2606.00991/) (2026)
- [CARVE: Certified Affordable Repair of Vetoed Maneuvers via Envelopes for Interactive Driving](https://mlsanity.com/paper/2606.02641/) (2026)
- [PixelPilot: Scalable Vision-Language-Action Models for End-to-End Autonomous Driving](https://mlsanity.com/paper/2607.04637/) (2026)
- [DispatchRAG: Grounding Emergency Dispatch Decisions in Real-World Protocols from Traffic Accident Video](https://mlsanity.com/paper/2607.23132/) (2026)
- [One Perception, All Maneuvers: Directional Traffic Signal Understanding for Maneuver-Level Signal Intent Prediction](https://mlsanity.com/paper/2609.32316/) (2026)
- [Beyond Retrieval Relevance: Scene-Grounded Risk Entailment for Vision-Language Driving](https://mlsanity.com/paper/2609.34145/) (2026)
- [Retrieval-Augmented Large Language Model Decision-Making for Autonomous Driving Guided by Chinese Philosophical Wisdom](https://mlsanity.com/paper/2610.03948/) (2026)
- [Large Language Model-Assisted Preparation of Transportation Management Plans: A Case Study with WisDOT WisTMP System](https://mlsanity.com/paper/2610.10650/) (2026)

## Abstract (arXiv)

Understanding and adhering to traffic regulations is essential for autonomous vehicles to ensure safety and trustworthiness. However, traffic regulations are complex, context-dependent, and differ between regions, posing a major challenge to conventional rule-based decision-making approaches. We present an interpretable, regulation-aware decision-making framework, DriveReg, which enables autonomous vehicles to understand and adhere to region-specific traffic laws and safety guidelines. The framework integrates a Retrieval-Augmented Generation (RAG)-based Traffic Regulation Retrieval Agent, which retrieves relevant rules from regulatory documents based on the current situation, and a Large Language Model (LLM)-powered Reasoning Agent that evaluates actions for legal compliance and safety. Our design emphasizes interpretability to enhance transparency and trustworthiness. To support systematic evaluation, we introduce the DriveReg Scenarios Dataset, a comprehensive dataset of driving scenarios across Boston, Singapore, and Los Angeles, with both hypothesized text-based cases and real-world driving data, constructed and annotated to evaluate models' capacity for regulation understanding and reasoning. We validate our framework on the DriveReg Scenarios Dataset and real-world deployment, demonstrating strong performance and robustness across diverse environments.