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A Car Driver Assistance Model Based on Neural Network and Hidden Markov Chain

Yuyan Huang, Jingbo Shao


Car driver assistance is very importance for drivers to understand road conditions, drive safely and reduce traffic accidents.This paper proposes a novel car driver assistance model based on neural network and hidden Markov Chain to achieve automatic safety alert and improve car's safety. The main factors of drivers’ driving state are analyzed thus obtaining three driving states of the initial decision vector for the BP neural network. Back propagation neural network for driving status recognition is established using built-in BP model by Simulink in Matlab. The well-trained results by BPNN are taken as the parameters for Hidden Markov chain Model to construct the assistant driving model. The proposed method has been applied onto the simulation platform, experimental results demonstrate the presented approach achieves significant security guarantee for car driving.


driving status recognition, back propagation neural network, hidden Markov chain

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