# TARGET: Automated Scenario Generation from Traffic Rules for Testing Autonomous Vehicles via Validated LLM-Guided Knowledge Extraction

arXiv: https://arxiv.org/abs/2305.06018 · HTML: https://mlsanity.com/paper/2305.06018/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2023.

## Built on by (most cited first, 9 of 9)

- [Hybrid Reasoning Based on Large Language Models for Autonomous Car Driving](https://mlsanity.com/paper/2402.13602/) (2024)
- [Generative transformations and patterns in LLM-native approaches for software verification and falsification](https://mlsanity.com/paper/2404.09384/) (2024)
- [AutoMT: A Multi-Agent LLM Framework for Automated Metamorphic Testing of Autonomous Driving Systems](https://mlsanity.com/paper/2510.19438/) (2025)
- [Learning from Risk: LLM-Guided Generation of Safety-Critical Scenarios with Prior Knowledge](https://mlsanity.com/paper/2511.20726/) (2025)
- [PlannerForge: LLM Agents for Scenario-Based Testing of Motion Planners in Autonomous Driving](https://mlsanity.com/paper/2609.08965/) (2026)
- [Natural Adversaries: Fuzzing Autonomous Vehicles with Realistic Roadside Object Placements](https://mlsanity.com/paper/2409.10562/) (2024)
- [MARL-OT: Multi-Agent Reinforcement Learning Guided Online Fuzzing to Detect Safety Violation in Autonomous Driving Systems](https://mlsanity.com/paper/2501.14451/) (2025)
- [TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation](https://mlsanity.com/paper/2606.29097/) (2026)
- [Teach-to-Crash: A Closed-Loop Student-Teacher LLM Framework for Collision-Inducing Test Scenario Generation](https://mlsanity.com/paper/2609.27296/) (2026)

## Abstract (arXiv)

Recent incidents with autonomous vehicles highlight the need for rigorous testing to ensure safety and robustness. Constructing test scenarios for autonomous driving systems (ADSs), however, is labor-intensive. We propose TARGET, an end-to-end framework that automatically generates test scenarios from traffic rules. To address complexity, we leverage a Large Language Model (LLM) to extract knowledge from traffic rules. To mitigate hallucinations caused by large context during input processing, we introduce a domain-specific language (DSL) designed to be syntactically simple and compositional. This design allows the LLM to learn and generate test scenarios in a modular manner while enabling syntactic and semantic validation for each component. Based on these validated representations, TARGET synthesizes executable scripts to render scenarios in simulation. Evaluated seven ADSs with 284 scenarios derived from 54 traffic rules, TARGET uncovered 610 rule violations, collisions, and other issues. For each violation, TARGET generates scenario recordings and detailed logs, aiding root cause analysis. Two identified issues were confirmed by ADS developers: one linked to an existing bug report and the other to limited ADS functionality.