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Comparability of driving automation crash databases.

Noah J Goodall¹

¹Virginia Transportation Research Council 530 Edgemont Road, Charlottesville, VA 22903, United States.

Journal of Safety Research

|February 22, 2025

Summary

Comparing driving automation (DA) and human-driven crash data reveals significant inconsistencies in data collection methods. Standardizing crash definitions and improving data collection are crucial for accurate safety assessments of automated vehicles.

Keywords:

Advanced driver assistance systems Autonomous vehicles Crash risk

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Area of Science:

Road safety research
Automotive engineering
Data science

Background:

Current driving automation (DA) systems and human-driven vehicle crash databases were reviewed.
The comparability of existing crash databases was evaluated.

Purpose of the Study:

To assess the consistency, scope, and potential biases in DA and human-driven crash data sources.
To explore alternative methods for determining vehicle automation capability from crash records.

Main Methods:

Reviewed five sources of DA crash data and three sources of human-driven crash data.
Analyzed inclusion criteria, scope, and bias in data collection.
Investigated using vehicle identification numbers (VINs) to determine automation capability.

Main Results:

Data sets exhibited incompatible minimum crash severity thresholds, hindering accurate crash rate comparisons.
The common "police-reportable crash" standard varies by jurisdiction.
Low- and no-damage crashes, frequent and statistically powerful, were inconsistently reported for automated vehicles.

Conclusions:

Standardizing crash definitions and improving data collection are essential for reliable safety evaluations.
Recommendations include collecting DA exposure data, using electronic data recorders, and standardizing crash definitions.
Findings can inform researchers, developers, and regulators for enhanced safety assessments and data collection policies.