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

Xu, Zhepu; Song, Ziyi; Zhang, Shiyu; Song, Jiatong; Dong, Yupu; Chen, Peiyan · 2026 · Crossref

DOI: 10.1155/atr/6396683

archive: archived pipeline: cataloged verified

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Summary

This study investigates the impact of Level 2 and Level 3 automated driving technology on traffic safety within highway work zones. As China’s road network enters a maintenance era, work zones have become common, yet they pose significant safety risks due to disrupted traffic flow. While fully automated vehicles (Level 5) are not yet commercially viable, Level 2/3 vehicles are increasingly prevalent. Unlike higher-level automation, Level 2/3 systems may disengage in complex work zone environments, requiring human takeover. This disengagement reduces the proportion of vehicles maintaining automated control and introduces new risks if manual takeover is delayed or inadequate. The research aims to quantify these safety impacts to inform future traffic control strategies and reduce work zone accidents. The researchers employed microscopic traffic simulation using the SUMO software, modeling a real work zone on the S20 Shanghai Outer Ring Expressway. To accurately represent Level 2/3 behavior, the study integrated models for automated driving (Adaptive Cruise Control), manual driving (Krauss model), and the transition of control, including driver performance recovery and Minimum Risk Maneuver (MRM) triggers. Safety was assessed using a comprehensive "all types of traffic conflict technology" that detects single-vehicle, two-vehicle, and multi-vehicle conflicts, quantified by the Unit Equivalent Traffic Conflict Number (UETCN). The experimental design utilized an orthogonal table to analyze the effects of market penetration rate (MPR), traffic volume, disengagement thresholds, takeover styles (aggressive, normal, conservative), large vehicle proportion, warning area length, and speed limits. The findings reveal that the impact of automated driving on work zone safety is complex and non-linear. Increasing the MPR of automated vehicles reduces the occurrence of single-vehicle conflicts but simultaneously increases the likelihood of multi-vehicle conflicts. Disengagement events, triggered by environmental complexity or system limitations, reduce the effective automated driving rate. If drivers fail to take over timely or adequately, the system triggers an MRM, which can disrupt traffic flow and create new conflict points. The study identifies that different factors exert varying degrees of influence on safety metrics, with disengagement detection and takeover mechanisms being critical determinants of overall safety outcomes. The significance of this research lies in its identification of specific risks associated with partial automation in work zones, challenging the assumption that higher MPR always leads to improved safety. The results suggest that future improvements should focus on optimizing disengagement detection algorithms and enhancing driver takeover mechanisms to mitigate the increased risk of multi-vehicle conflicts. By providing a theoretical basis for new traffic control methods, the study offers actionable insights for managing the mixed traffic of human-driven and partially automated vehicles in high-risk maintenance environments.

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Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• work zones
• situational awareness
• driverless ads
• traffic density
• emergency work zone conspicuity
• automation surprise

Information type

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• Empirical Findings: crash risk outcomes
• Theoretical Contribution: conceptual framework