# LawBreaker: An Approach for Specifying Traffic Laws and Fuzzing Autonomous Vehicles

arXiv: https://arxiv.org/abs/2208.14656 · HTML: https://mlsanity.com/paper/2208.14656/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2022.

## Built on by (most cited first, 16 of 16)

- [AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agents](https://mlsanity.com/paper/2503.18666/) (2025)
- [ProbGuard: Proactive Runtime Monitoring for LLM Agent Safety via Probabilistic Prediction](https://mlsanity.com/paper/2508.00500/) (2025)
- [Text2Scenario: Text-Driven Scenario Generation for Autonomous Driving Test](https://mlsanity.com/paper/2503.02911/) (2025)
- [From Dashcam Videos to Driving Simulations: Stress Testing Automated Vehicles against Rare Events](https://mlsanity.com/paper/2411.16027/) (2024)
- [EpiTESTER: Testing Autonomous Vehicles with Epigenetic Algorithm and Attention Mechanism](https://mlsanity.com/paper/2312.00207/) (2023)
- [Testing learning-enabled cyber-physical systems with Large-Language Models: A Formal Approach](https://mlsanity.com/paper/2311.07377/) (2023)
- [Autoware.Flex: Human-Instructed Dynamically Reconfigurable Autonomous Driving Systems](https://mlsanity.com/paper/2412.16265/) (2024)
- [Urban Computing in the Era of Large Language Models](https://mlsanity.com/paper/2504.02009/) (2025)
- [ADReFT: Adaptive Decision Repair for Safe Autonomous Driving via Reinforcement Fine-Tuning](https://mlsanity.com/paper/2506.23960/) (2025)
- [Argus: Resilience-Oriented Safety Assurance Framework for End-to-End ADSs](https://mlsanity.com/paper/2511.09032/) (2025)
- [Natural Adversaries: Fuzzing Autonomous Vehicles with Realistic Roadside Object Placements](https://mlsanity.com/paper/2409.10562/) (2024)
- [STCLocker: Deadlock Avoidance Testing for Autonomous Driving Systems](https://mlsanity.com/paper/2506.23995/) (2025)
- [TigAug: Data Augmentation for Testing Traffic Light Detection in Autonomous Driving Systems](https://mlsanity.com/paper/2507.05932/) (2025)
- [ROMAN: Reward-Orchestrated Multi-Head Attention Network for Autonomous Driving System Testing](https://mlsanity.com/paper/2602.05629/) (2026)
- [TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation](https://mlsanity.com/paper/2606.29097/) (2026)
- [MakSafe: A Spatiotemporal Semantic Framework for Formal Specification and Compliance Checking of Road Vehicle Safety Rules](https://mlsanity.com/paper/2610.19735/) (2026)

## Abstract (arXiv)

Autonomous driving systems (ADSs) must be tested thoroughly before they can be deployed in autonomous vehicles. High-fidelity simulators allow them to be tested against diverse scenarios, including those that are difficult to recreate in real-world testing grounds. While previous approaches have shown that test cases can be generated automatically, they tend to focus on weak oracles (e.g. reaching the destination without collisions) without assessing whether the journey itself was undertaken safely and satisfied the law. In this work, we propose LawBreaker, an automated framework for testing ADSs against real-world traffic laws, which is designed to be compatible with different scenario description languages. LawBreaker provides a rich driver-oriented specification language for describing traffic laws, and a fuzzing engine that searches for different ways of violating them by maximising specification coverage. To evaluate our approach, we implemented it for Apollo+LGSVL and specified the traffic laws of China. LawBreaker was able to find 14 violations of these laws, including 173 test cases that caused accidents.