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Cracking the Code of Car Crashes: How Autonomous and Human Driving Differ in Risk Factors

Author

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  • Shengyan Qin

    (Department of Big Data Management and Application, School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China)

  • Li Liu

    (Department of Big Data Management and Application, School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China)

Abstract

With the rapid advancement of autonomous driving (AD) technology, its application in road traffic has garnered increasing attention. This study analyzes 534 AD and 82,030 human driver traffic accidents and employs SMOTE to balance the sample sizes between the two groups. Using association rule mining, this study identifies key risk factors and behavioral patterns. The results indicate that while both AD and human driver accidents exhibit seasonal trends, their risk characteristics and distributions differ markedly. AD accidents are more frequent in summer (July–August) on clear days and tend to occur at intersections and on streets, with a higher proportion of non-injury collisions observed at night. Collisions involving non-motorized road users are more prevalent in human driver accidents. AD systems show certain advantages in detecting non-motorized vehicles and performing low-speed evasive maneuvers, particularly at night; however, limitations remain in perception and decision-making under complex conditions. Human driver accidents are more susceptible to driver-related factors such as fatigue, distraction, and risk-prone behaviors. Although AD accidents generally result in lower injury severity, further technological refinement and scenario adaptability are required. This study provides insights and recommendations to enhance the safety performance of both AD and human-driven systems, offering valuable guidance for policymakers and developers.

Suggested Citation

  • Shengyan Qin & Li Liu, 2025. "Cracking the Code of Car Crashes: How Autonomous and Human Driving Differ in Risk Factors," Sustainability, MDPI, vol. 17(10), pages 1-25, May.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:10:p:4368-:d:1653699
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    References listed on IDEAS

    as
    1. Ji, Yuanjin & Huang, Youpei & Zeng, Junwei & Ren, Lihui & Chen, Yuejian, 2025. "A physical‒data-driven combined strategy for load identification of tire type rail transit vehicle," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    2. Ji, Yuanjin & Huang, Youpei & Yang, Maozhenning & Leng, Han & Ren, Lihui & Liu, Hongda & Chen, Yuejian, 2025. "Physics-informed deep learning for virtual rail train trajectory following control," Reliability Engineering and System Safety, Elsevier, vol. 261(C).
    3. Ehsan Kohanpour & Seyed Rasoul Davoodi & Khaled Shaaban, 2024. "Analyzing Autonomous Vehicle Collision Types to Support Sustainable Transportation Systems: A Machine Learning and Association Rules Approach," Sustainability, MDPI, vol. 16(22), pages 1-41, November.
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