Research on the vehicle-borne information fusion strategy based on big data analysis. (5th August 2019)

• Record Type: Journal Article
• Authors: Miao, Yingkai
• Is Part Of: International journal of vehicle information and communication systems. Volume 4:Number 2(2019)
• Page Start: 187
• Page End: 201
• Publication Date: 2019-08-05
• Subjects: big data analysis -- vehicle-borne information fusion -- interaction -- maximum entropy theory -- CSIMM algorithm; Automobiles -- Electronic equipment -- Periodicals; 629.27
• Languages: English
• ISSNs: 1471-0242

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.