CRASH: Cognitive Reasoning Agent for Safety Hazards in Autonomous Driving

Erick Silva; Rehana Yasmin; Ali Shoker

cs.AIMar 16, 2026

CRASH: Cognitive Reasoning Agent for Safety Hazards in Autonomous Driving

Authors: Erick Silva, Rehana Yasmin, Ali Shoker

Organizations: King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

Abstract

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.

Figures & tables

Work	Stats	Expert Rules	NLP/LLM	Scalable	Sys. Reason
Favaro et al. [ 7 ]	$\checkmark$	–	–	–	–
Houseal et al. [ 11 ]	$\checkmark$	–	–	–	–
Shah et al. [ 21 ]	–	$\checkmark$	–	–	$\checkmark$
Zhang et al. [ 27 ]	$\checkmark$	–	$\checkmark$	$\checkmark$	–
CRASH (Ours)	$\checkmark$	$\checkmark$	$\checkmark$	$\checkmark$	$\checkmark$

Table 1: Methodological comparison of selected prior work and CRASH.

Figure 1: CRASH Architecture. During preprocessing, the database is filtered for incomplete entries and unified into four columns. Then, each row is sent to Processing through the LLM. We finally aggregate all information into a CSV file and send it to Postprocessing, where the data is used to generate our analysis and simulation descriptions.

Model	Make	Cases	%
Jaguar I-Pace	Waymo LLC	1015	46.83%
Cruise Av	GM LLC / Cruise LLC	355	16.37%
Jaguar I-Pace	Transdev	172	7.93%
Honda Civic	Honda	113	5.21%
Toyota Highlander	Zoox, Inc.	88	4.06%
Others	Various	425	19.60%

Table 2: Distribution of Incident Cases by Reporting Entity

Dataset Category	Original Cases	Final Cases
ADS	1790	1764
ADAS	2582	352
Others	3520	52
Total	7892	2168

Table 3: Case distribution before and after filtering

Panel of Figure 2 — Figure 2: System prompt for the CRASH Agent, incorporating heuristic rules and one-shot examples.

Figure 3: Compact taxonomy of AV incident causes.

Method	AV Fail	Late AI	Cause	Sys. Fail
Majority class	54%	34%	42%	22%
Keyword rules	48%	46%	44%	18%
CRASH (ours)	86%	84%	76%	46%

Table 4: Baseline comparison on the 50 expert-evaluated cases (lenient criterion).

Figure 4: Primary cause distribution and subsystem breakdown across 2,168 AV incidents.

Figure 5: Overlap between late AI behavior, AV failures, and rear-end collisions ( $N{=}2{,}168$ ).

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