CNN and KPCA-Based Automated Feature Extraction for Real Time Driving Pattern Recognition

Driving conditions greatly affect the energy control and the fuel economy of a hybrid electric vehicle (HEV). In this paper, an automated feature extraction scheme based on convolution neural networks (CNNs) and Kernel PCA (KPCA) for real time driving pattern recognition (RTDPR) is proposed in order to achieve consistent performance of the energy management. Firstly, a dimension expanding strategy is performed to transform one-dimensional speed sequences to generate a two-dimensional dataset. Then, the transformed data is sent to the CNN and KPCA based feature extractor. Finally, the feature extractor automatically selects the most representative features for classification. To improve the generalization of CNN to a small sample dataset, the structure of the typical CNN is adjusted by adding the KPCA layer in order to reduce model parameters. The model is well trained and evaluated in simulation, and it is tested for RTDPR in the real world. Simulation and experimental results show that the proposed automated feature extraction strategy outperforms the conventional driving pattern recognition algorithms based on manually feature extraction, which has achieved the state-of-the-art recognition accuracy.