Multi-sensor fusion algorithm in cooperative vehicle-infrastructure system for blind spot warning

With the rapid development of electric vehicles and artificial intelligence technology, the automatic driving industry has entered a rapid development stage. However, there is a risk of traffic accidents due to the blind spot of vision, whether autonomous vehicles or traditional vehicles. In this ar...

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Veröffentlicht in:International journal of distributed sensor networks Jg. 18; H. 5; S. 155013292211004
Hauptverfasser: Xiang, Chao, Zhang, Li, Xie, Xiaopo, Zhao, Longgang, Ke, Xin, Niu, Zhendong, Wang, Feng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London, England SAGE Publications 01.05.2022
Wiley
Schlagworte:
5G
Multi-sensor fusion
blind spot warning
RSU
V2X
ISSN:1550-1329, 1550-1477
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Zusammenfassung:With the rapid development of electric vehicles and artificial intelligence technology, the automatic driving industry has entered a rapid development stage. However, there is a risk of traffic accidents due to the blind spot of vision, whether autonomous vehicles or traditional vehicles. In this article, a multi-sensor fusion perception method is proposed, in which the semantic information from the camera and the range information from the LiDAR are fused at the data layer and the LiDAR point cloud containing semantic information is clustered to obtain the type and location information of the objects. Based on the sensor equipments deployed on the roadside, the sensing information processed by the fusion method is sent to the nearby vehicles in real-time through 5G and V2X technology for blind spot early warning, and its feasibility is verified by experiments and simulations. The blind spot warning scheme based on roadside multi-sensor fusion perception proposed in this article has been experimentally verified in the closed park, which can obviously reduce the traffic accidents caused by the blind spot of vision, and is of great significance to improve traffic safety.
ISSN:1550-1329
1550-1477
DOI:10.1177/15501329221100412