Traffic collisions early warning aided by small unmanned aerial vehicle companion

Hao Luo, Shu-Chuan Chu (), Xiaofeng Wu, Zhenfei Wang and Fangqian Xu
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Hao Luo: Zhejiang University
Shu-Chuan Chu: Flinders University of South Australia
Xiaofeng Wu: The University of Sydney
Zhenfei Wang: Space Star Technology Co., Ltd.
Fangqian Xu: Zhejiang University of Media and Communications

Telecommunication Systems: Modelling, Analysis, Design and Management, 2020, vol. 75, issue 2, No 4, 169-180

Abstract: Abstract Most traffic surveillance systems are based on videos which captured by fixed cameras on bridges, intersections, etc. However, many traffic collisions may occur in many places without such surveillance systems, e.g., in rural highway. Researchers have developed a set of techniques to improve safety on these places, while it is still not enough to reduce collision risk. Based on a novel concept, this paper proposes a traffic collisions early warning scheme aided by small unmanned aerial vehicle (UAV) companion. Basically, it is a vision-based driver assistance system, and the difference in comparison with the available schemes lies in the camera is flying along with the host vehicle. In particular, the system’s framework and the vision-based vehicle collision detection algorithm are proposed. The small UAV works in two switchable modes, i.e., high speed flight or low speed motion. The high speed flight corresponds to the host vehicle moving in highway, while the low speed motion includes hover, vertical takeoff and landing. In addition, as the on-line machine learning is applied, the detection procedure can be implemented in real-time, which is critical in practical applications. Extensive experimental results and examples demonstrate the effectiveness of the proposed method, and its real-time performance outperforms typical tracking methods such as that based on Gaussian mixture model. Moreover, this scheme can be easily extended for some other similar application scenarios.

Keywords: Traffic collisions early warning; Unmanned aerial vehicle; Object detection and tracking (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s11235-015-0131-5

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