# CRASH: Cognitive Reasoning Agent for Safety Hazards in Autonomous Driving

arXiv: https://arxiv.org/abs/2603.15364 · HTML: https://mlsanity.com/paper/2603.15364/

## Key facts

- Cited by 1 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 9 of its 28 references that are in the map.
- First posted on arXiv 16 Mar 2026.

## Placed in

- LLM reasoning / [Chain-of-thought reasoning for driving](https://mlsanity.com/lr/chain-of-thought-reasoning-for-driving/)
- End-to-end autonomous driving / [VLMs for autonomous driving](https://mlsanity.com/driving/vlms-for-autonomous-driving/)
- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 9 of 9)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [X-lifecycle Learning for Cloud Incident Management using LLMs](https://mlsanity.com/paper/2404.03662/) (2024)
- [Disengagement Cause-and-Effect Relationships Extraction Using an NLP Pipeline](https://mlsanity.com/paper/2111.03511/) (2021)
- [Lessons from the Cruise Robotaxi Pedestrian Dragging Mishap](https://mlsanity.com/paper/2406.05281/) (2024)
- [Developing a Taxonomy of Elements Adversarial to Autonomous Vehicles](https://mlsanity.com/paper/2403.00136/) (2024)
- [Improving Crash Data Quality with Large Language Models: Evidence from Secondary Crash Narratives in Kentucky](https://mlsanity.com/paper/2508.04399/) (2025)

## Built on by (most cited first, 1 of 1)

- [SeFaR: Semantic Feature-aware Robustness Testing of Deep Neural Networks](https://mlsanity.com/paper/2608.10289/) (2026)

## Abstract (arXiv)

As AVs grow in complexity and diversity, identifying the root causes of operational failures has become increasingly complex. The heterogeneity of system architectures across manufacturers, ranging from end-to-end to modular designs, together with variations in algorithms and integration strategies, limits the standardization of incident investigations and hinders systematic safety analysis. This work examines real-world AV incidents reported in the NHTSA database. We curate a dataset of 2,168 cases reported between 2021 and 2025, representing more than 80 million miles driven. To process this data, we introduce CRASH, Cognitive Reasoning Agent for Safety Hazards, an LLM-based agent that automates reasoning over crash reports by leveraging both standardized fields and unstructured narrative descriptions. CRASH operates on a unified representation of each incident to generate concise summaries, attribute a primary cause, and assess whether the AV materially contributed to the event. Our findings show that (1) CRASH attributes 64% of incidents to perception or planning failures, underscoring the importance of reasoning-based analysis for accurate fault attribution; and (2) approximately 50% of reported incidents involve rear-end collisions, highlighting a persistent and unresolved challenge in autonomous driving deployment. We further validate CRASH with five domain experts, achieving 86% accuracy in attributing AV system failures. Overall, CRASH demonstrates strong potential as a scalable and interpretable tool for automated crash analysis, providing actionable insights to support safety research and the continued development of autonomous driving systems.