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Pattern recognition in crash clusters involving vehicles with advanced driving technologies.

Reuben Tamakloe¹, Mahdi Khorasani¹, Subasish Das²

¹The Cho Chun Shik Graduate School of Mobility, Korea Advanced Institute of Science and Technology, 193 Munji-ro, Yuseong-gu, Daejeon 34051, South Korea.

Accident; Analysis and Prevention

|May 7, 2025

Summary

Autonomous Vehicle (AV) crashes are rising. This study analyzed Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) crashes, finding distinct risk patterns for each system to improve safety.

Keywords:

Advanced driver assistance systems Automated driving systems Automated vehicle Crash Machine learning

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Published on: September 18, 2012

Area of Science:

Road safety
Automotive engineering
Human-computer interaction

Background:

Autonomous Vehicle (AV) technologies, including Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS), offer potential safety benefits but are involved in increasing numbers of crashes.
Existing research on AV crash factors has not fully elucidated critical risk patterns within ADAS- and ADS-specific crash clusters.
Understanding these patterns is crucial for targeted safety improvements and policy development.

Purpose of the Study:

To identify and analyze critical risk factor patterns in clusters of ADAS- and ADS-engaged AV crashes.
To compare and contrast crash characteristics between ADAS- and ADS-engaged scenarios.
To provide data-driven recommendations for enhancing AV safety and informing policy.

Main Methods:

Utilized the Cluster Correspondence Analysis tool to cluster crash-related factors for ADAS and ADS engaged AVs.
Analyzed crash data to identify distinct clusters and their associated characteristics.
Compared crash outcomes, road conditions, and crash partners between ADAS and ADS engagement.

Main Results:

Identified three distinct crash clusters for both ADAS and ADS engagement.
ADAS-engaged crashes frequently involve fatal intersection collisions, especially with left-turning vehicles.
ADS-engaged crashes commonly involve non-motorists in daylight; both systems show risks with animals on high-speed roads.

Conclusions:

Infrastructural improvements and enhanced AV algorithms/sensors (especially for non-motorist and animal detection in low light) are recommended.
Policymakers should focus on driver education for safe AV operation and mandate external human-machine interfaces to improve communication and reduce rear-end collisions.