Hesitant convolutional neural networks and intelligent drive algorithm fused subjective guidance

In response to the low-carbon development strategy, the new energy vehicle has become one crucial and recognized tool to satisfy the emission reduction requirements, the representative advance of the new energy vehicle is its intelligent drive technique. This paper introduces the driver’s subjective guidance to further improve the intelligent drive technique. To do this, the hesitant convolutional neural networks (HCNN) are proposed to deal with the above issue, in which the hesitant fuzzy set is used to fully describe the subjective guidance information. Thus, the different driver’s personalized needs can be considered and fused into the intelligent drive algorithm to achieve the intelligent drive technique of new energy vehicles. Note that the innovation and difference of the proposed method are the subjective guidance fusion and its generalized presentation but not the intelligent drive calculation. After that, the data downward partition model and weight secondary matching calculation are developed based on the HCNN to fully describe and process the hesitant fuzzy subjective guidance information. Furthermore, the intelligent drive algorithm considering the driver’s subjective guidance is provided according to the above methods. Finally, an illustrative example is given to show the effectiveness of the proposed algorithm in the given drive scenario.