Application of Two-Level Joint Information Fusion Model in Intelligent Vehicle

Yan Ting Lan, Jiinying Huang, Xiaodong Chen


This paper proposes a two-level joint information fusion model combining BP neural network and D-S evidence theory. The model of great practical value reduces target identification error probability by multiple features of the target information, shows good scalability with its two steps of information fusion model, and conveniently increases/reduces feature fusion information source according to different situations and different objects. The method used for intelligent vehicles has good flexibility and robustness in tracking and avoiding obstacle. The simulation and real vehicle tests have verified effectiveness of the method.


information fusion, intelligent vehicle, neural network, D-S evidence theory

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International Journal of Online and Biomedical Engineering (iJOE) – eISSN: 2626-8493
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