Automated detection and analysis technology for traffic conflicts based on unmanned aerial vehicle video data

GUO Yan-yong; LUO Yuan-wei; DAI Shuai; LIU Pan

doi:10.19818/j.cnki.1671-1637.2026.038

Volume 26 Issue 4

Apr. 2026

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GUO Yan-yong, LUO Yuan-wei, DAI Shuai, LIU Pan. Automated detection and analysis technology for traffic conflicts based on unmanned aerial vehicle video data[J]. Journal of Traffic and Transportation Engineering, 2026, 26(4): 167-183. doi: 10.19818/j.cnki.1671-1637.2026.038

Citation:

GUO Yan-yong, LUO Yuan-wei, DAI Shuai, LIU Pan. Automated detection and analysis technology for traffic conflicts based on unmanned aerial vehicle video data[J]. Journal of Traffic and Transportation Engineering, 2026, 26(4): 167-183. doi: 10.19818/j.cnki.1671-1637.2026.038

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Automated detection and analysis technology for traffic conflicts based on unmanned aerial vehicle video data

doi: 10.19818/j.cnki.1671-1637.2026.038

1.
School of Transportation, Southeast University, Nanjing 211189, Jiangsu, China
2.
Research Institute for Road Safety of MPS, Beijing 100176, China

Funds:

Young Scientists Fund of the National Natural Science Foundation of China (Category A, including continuation funding projects) 52525204

National Key R&D Program of China 2023YFB4302701

More Information

Corresponding author: LIU Pan, professor, PhD, E-mail: liupan@seu.edu.cn
Received Date: 2025-01-22
Accepted Date: 2025-09-26
Rev Recd Date: 2025-07-28
Publish Date: 2026-04-28

Abstract

Abstract

To achieve rapid and automated extraction of traffic conflicts, a traffic conflict detection and extraction method based on unmanned aerial vehicle (UAV) video was proposed. A video stabilization method integrating feature point extraction and matching algorithms was developed to eliminate frame-to-frame translation and rotation caused by UAV flight jitter and to ensure stable vehicle trajectory extraction. A rotated vehicle detection algorithm based on the YOLOv8-OBB model was developed and combined with the ByteTrack tracking method, where high- and low-confidence detection boxes were fused to enable continuous extraction of vehicle trajectories. The Savitzky-Golay filter was adopted to denoise and smooth the trajectories, thus retaining the original features of the trajectories and eliminating noise. A traffic conflict detection and classification method based on vehicle trajectories was developed, using bounding box-based approaches to calculate TTC, PET, and MTTC indicators and improve detection accuracy. Invalid conflicts were eliminated through a rationality verification mechanism, and angular conflicts, lateral conflicts, and rear-end conflicts were distinguished according to the vehicle heading angle and the predicted collision position at the time of conflict. A case study was conducted at three signalized intersections in Nanjing. The results show that traffic conflicts can be extracted rapidly and accurately from videos by the automatic traffic conflict detection method, with a detection accuracy of 92%. The average processing speeds of the video stabilization algorithm, trajectory extraction algorithm, and conflict extraction algorithm are 3.64 frames per second, 5.38 frames per second, and 250 frames per second, respectively, which meet the requirements for rapid analysis of large-scale video data. The results help improve the quality of traffic conflict data and provide reliable data support for traffic safety research based on traffic conflicts, demonstrating broad application potential.
- low-altitude traffic,
- traffic conflict,
- automated detection,
- UAV video analysis,
- rotated object detection,
- traffic safety

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References

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Automated detection and analysis technology for traffic conflicts based on unmanned aerial vehicle video data

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