Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Discrimination of the road condition toward understanding of vehicle driving environments
IEEE Transactions on Intelligent Transportation Systems
Personalized driving behavior monitoring and analysis for emerging hybrid vehicles
Pervasive'12 Proceedings of the 10th international conference on Pervasive Computing
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As a new kind of vehicles with low fuel cost and low emission, hybrid electric vehicle (REV) has been given more and more attentions in recent years. The key technique in the HEV is adopting the optimal control strategy for the best performance. As the premise, a correct driving condition discrimination has an extremely important significance. This paper proposes an intelligent multi-feature statistical approach to discriminate the driving conditions of the HEV automatically. First of all, this approach samples the driving cycle periodically. Then it extracts multiple statistical features and tests their significance by statistical analysis. After that, it applies SVM and other machine learning methods to discriminate the driving conditions intelligently and automatically. Compared to the others, the proposed approach can compute fast and discriminate in real time during the whole HEV running. In our experiments, it reaches an accuracy of 97%. As a result, our approach can mine the valid information in the data completely and extract multiple features which have clear meanings and significance. Finally, according to the prediction experiment by a neural network and the fitting experiment by the ARMA model, it turns out that our proposed approach raises the efficiency of controlling the HEV considerably.