Vision-Based Collision Prevention on Construction Sites: Integrating Trajectory Prediction and Uncertainty Modeling

Collisions between workers and vehicles are a leading cause of fatalities on construction sites around the world. While automation technology has been extensively applied to various aspects of construction safety management, including collision prevention, significant inaccuracies in estimating safe distances between workers and vehicles, coupled with limited hazard prediction capabilities, continue to hinder efforts to effectively prevent collision accidents on construction sites. To address these challenges, this study proposes a novel collision prevention method that integrates computer vision and trajectory prediction technologies. First, the You Only Look Once version 11 (YOLOv11) model and simple online and realtime tracking (SORT) algorithm were employed to accurately detect and track workers and vehicles, effectively extracting their movement trajectories in dynamic construction scenarios. Second, a transformer-based trajectory prediction algorithm was developed, enabling high-precision motion forecasting and providing critical data for risk region definition and collision warning. Finally, a dynamic risk region division method was designed, incorporating motion states and positional uncertainty to identify potential collision risks in real time. Experimental results demonstrated that the proposed system achieved over 90% accuracy in detecting workers and vehicles and successfully extracted their trajectories. The transformer-based prediction algorithm showed excellent short-term accuracy, providing reliable trajectory forecasts. Furthermore, the risk region definition method effectively identified potential collision areas, including those occurring during vehicle turning maneuvers. In summary, the proposed method performs well in complex scenarios, accurately predicting risk regions, including collision risks during vehicle turning maneuvers. This study provides a reliable and efficient solution for proactive collision prevention, offering robust technical support for improving safety management and reducing accidents in construction environments.

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Publication Details

Journal
Journal of Construction Engineering and Management
Published
2025-10-23
DOI
https://doi.org/10.1061/jcemd4.coeng-16894
Primary Topic
Occupational Health and Safety Research
Type
article
Field-Weighted Citation Impact
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article

Vision-Based Collision Prevention on Construction Sites: Integrating Trajectory Prediction and Uncertainty Modeling

Zeli Wang, Qinyuan Liu, Xuzhong Yan, Heng Li et al.
Journal of Construction Engineering and Management
Occupational Health and Safety Research
article

Vision-Based Collision Prevention on Construction Sites: Integrating Trajectory Prediction and Uncertainty Modeling

Zeli Wang, Qinyuan Liu, Xuzhong Yan, Heng Li, Xiaoyan Ma
article en

Abstract

Collisions between workers and vehicles are a leading cause of fatalities on construction sites around the world. While automation technology has been extensively applied to various aspects of construction safety management, including collision prevention, significant inaccuracies in estimating safe distances between workers and vehicles, coupled with limited hazard prediction capabilities, continue to hinder efforts to effectively prevent collision accidents on construction sites. To address these challenges, this study proposes a novel collision prevention method that integrates computer vision and trajectory prediction technologies. First, the You Only Look Once version 11 (YOLOv11) model and simple online and realtime tracking (SORT) algorithm were employed to accurately detect and track workers and vehicles, effectively extracting their movement trajectories in dynamic construction scenarios. Second, a transformer-based trajectory prediction algorithm was developed, enabling high-precision motion forecasting and providing critical data for risk region definition and collision warning. Finally, a dynamic risk region division method was designed, incorporating motion states and positional uncertainty to identify potential collision risks in real time. Experimental results demonstrated that the proposed system achieved over 90% accuracy in detecting workers and vehicles and successfully extracted their trajectories. The transformer-based prediction algorithm showed excellent short-term accuracy, providing reliable trajectory forecasts. Furthermore, the risk region definition method effectively identified potential collision areas, including those occurring during vehicle turning maneuvers. In summary, the proposed method performs well in complex scenarios, accurately predicting risk regions, including collision risks during vehicle turning maneuvers. This study provides a reliable and efficient solution for proactive collision prevention, offering robust technical support for improving safety management and reducing accidents in construction environments.

Journal of Construction Engineering and ManagementVol. 152(1)
Tongji University (CN), Hong Kong Polytechnic University (HK), East China University of Science and Technology (CN), Zhejiang University of Technology (CN)
Openalex Percentile: Top 57%
Occupational Health and Safety Research
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