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Characteristics of media-reported road traffic crashes involving low-level automated vehicles in China, 2015–2025
Journal article   Open access   Peer reviewed

Characteristics of media-reported road traffic crashes involving low-level automated vehicles in China, 2015–2025

Min Zhao, Lei Yang, Hengjia Qi, David C Schwebel, Wangxin Xiao, Yu Wu, Peixia Cheng and Guoqing Hu
Journal of global health, Vol.16, 04245
08/28/2026
DOI: 10.7189/jogh.16.04245
PMCID: PMC13521493
PMID: 42659043
url
https://doi.org/10.7189/jogh.16.04245View
Published (Version of record) Open Access

Abstract

Background Low-level automated vehicles (LAVs) have emerged as a new public health challenge, but the epidemiological characteristics of LAV-related crashes remain unknown. Methods Based on media reports collected by the Automated Road Traffic Crash Data Platform (ARTCDP), we analysed the characteristics of LAV-related crashes in China between 1 January 2015 and 31 August 2025. Results The ARTCDP captured 4,669 crashes involving LAVs and 324,869 involving other motor vehicles. Compared to other motor vehicles, LAVs were more frequently reported to crash during nighttime (65.3% vs. 29.1%; P < 0.001), on expressways (31.9% vs. 20.2%; P < 0.001), on straight roads without junctions (50.7% vs. 29.4%; P < 0.001), and on rainy days (57.7% vs. 53.6%; P < 0.001). They were primarily reported to crash in economically developed regions, with those in Zhejiang, Guangdong, and Shanghai accounting for 31.6% of all crashes involving LAVs. Furthermore, 20.3% of the LAVs crashes and 16.3% of other motor vehicles crashes were associated with two or more factors. Brake-related issues (48.0% vs. 26.1%; P < 0.01), hazardous road surface condition (55.6% vs. 47.9%; P < 0.01), and distracted driving behaviour (28.7% vs. 9.8%; P < 0.01) more frequently occurred in LAV-involved crashes. Conclusions Internet-based media reports detected distinct characteristics of road traffic crashes involving LAVs, meriting the attention of policymakers and law enforcement.
 road traffic crash low-level automated vehicles media injury China

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