Traffic collisions early warning aided by small unmanned aerial vehicle companion.

Luo, Hao; Chu, Shu-Chuan; Wu, Xiaofeng; Wang, Zhenfei; Xu, Fangqian

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.

DRONE aircraft; HIGH-speed aeronautics; DRIVER assistance systems; GAUSSIAN mixture models; TRAFFIC monitoring; ROAD interchanges & intersections; VERTICALLY rising aircraft

Telecommunication Systems, 2020, Vol 75, Issue 2, p169

1018-4864

Academic Journal

10.1007/s11235-015-0131-5