Title: Research on the vehicle-borne information fusion strategy based on big data analysis

Authors: Yingkai Miao

Addresses: Puyang Vocational and Technical College, Puyang, Henan 457000, China

Abstract: In view of the disadvantages of the existing fusion interaction methods for vehicle-borne information, such as low accuracy and poor stability of information interaction, a new vehicle-borne information fusion interaction method based on big data analysis is proposed. Firstly, the noise in vehicle-borne information is filtered by wavelet transform, and then the maximum entropy theory is used for fusion. The fused vehicle-borne information is taken as the interactive sample, and the improved interactive multi-model algorithm is adopted to realise the fused interaction of vehicle-borne information. The experimental results show that the efficiency of the proposed method is above 97.8%, the stability is above 0.942, and the transmission delay is below 0.327 s. Therefore, in the process of vehicle-borne information fusion interaction, the use of big data analysis technology can make the vehicle-borne information interaction more efficient and more stable, and have more information coverage.

Keywords: big data analysis; vehicle-borne information fusion; interaction; maximum entropy theory; CSIMM algorithm.

DOI: 10.1504/IJVICS.2019.101515

International Journal of Vehicle Information and Communication Systems, 2019 Vol.4 No.2, pp.187 - 201

Received: 08 Dec 2018
Accepted: 06 Feb 2019
Published online: 11 Aug 2019 *

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