Impact of Level 2/3 Automated Driving Technology on Road Work Zone Safety

• URL: http://arxiv.org/abs/2503.07634v1
• Date: Wed, 05 Mar 2025 00:26:53 GMT
• Title: Impact of Level 2/3 Automated Driving Technology on Road Work Zone Safety
• Authors: Zhepu Xu, Ziyi Song, Yupu Dong, Peiyan Chen,
• Abstract summary: This paper explores the impact of Level 2/3 automated driving technology on road safety in high-speed highway work zone environments.<n>The study found that the impact of automated driving technology on work zone safety is complex.
• Score: 0.09642500063568188
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As China's road network enters the maintenance era, work zones will become a common sight on the roads. With the development of automated driving, vehicles equipped with Level 2/3 automated driving capabilities will also become a common presence on the roads. When these vehicles pass through work zones, automated driving may disengage, which can have complex effects on traffic safety. This paper explores the impact of Level 2/3 automated driving technology on road safety in high-speed highway work zone environments. Through microscopic traffic simulation method and using full-type traffic conflict technique, factors such as market penetration rate (MPR), traffic volume level, disengagement threshold, and driver takeover style are studied to understand their impact on work zone safety. The study found that the impact of automated driving technology on work zone safety is complex. Disengagement of automated vehicles in work zones reduces the proportion of vehicles that can maintain automated driving status. If takeover is not timely or adequate, it can easily lead to new traffic conflicts. Different factors have varying degrees of impact on work zone safety. Increasing MPR helps reduce the occurrence of single-vehicle conflicts, but it also increases the possibility of multi-vehicle conflicts. Therefore, future research and improvement directions should focus on optimizing the disengagement detection and takeover mechanisms of automated driving systems.

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