Title: Intelligent acquisition model of traffic congestion information in the vehicle networking environment based on multi-sensor fusion

Authors: Kun Jiang; Dan Su; Yanfu Zheng

Addresses: College of Computer and Information Engineering, Heihe University, Heihe 164300, China ' College of Computer and Information Engineering, Heihe University, Heihe 164300, China ' College of Computer and Information Engineering, Heihe University, Heihe 164300, China

Abstract: Aiming at the problems of low bandwidth, poor anti-interference ability and low-detection accuracy of traditional multi-path coherent vehicle network model, an intelligent acquisition model of traffic congestion information in vehicle network environment based on multi-features is proposed. The model clusters the traffic congestion information in the vehicle network and uses the multi-sensor fusion identification method to mine the traffic flow. In the vehicle networking environment, the vehicle traffic congestion information is analysed by information fusion theory, cross-fusion, text information, location information, image, audio, video and other information-aware technologies, and the coherent matching filter detector is used to realise the detection of traffic congestion information. And data collection, combined with knowledge base and database construction, is used to achieve intelligent collection and storage of traffic congestion information. The simulation results show that the model has high traffic congestion information acquisition accuracy and good feature matching ability, improves the intelligent acquisition ability, and detects the traffic congestion information in the vehicle network environment, thus guiding traffic scheduling.

Keywords: vehicle networking; traffic congestion information; acquisition; information fusion; data mining.

DOI: 10.1504/IJVICS.2019.101512

International Journal of Vehicle Information and Communication Systems, 2019 Vol.4 No.2, pp.155 - 169

Received: 08 Dec 2018
Accepted: 22 Jan 2019

Published online: 05 Aug 2019 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article