Hybrid-learning-based classification and quantitative inference of driver braking intensity of an electrified vehicle

• Main Authors: Lv, Chen, Xing, Yang, Lu, Chao, Liu, Yahui, Guo, Hongyan, Gao, Hongbo, Cao, Dongpu
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2019
• Subjects: Hybrid Learning; Random Forest; Engineering::Electrical and electronic engineering
• Online Access: https://hdl.handle.net/10356/105882; http://hdl.handle.net/10220/50054; http://dx.doi.org/10.1109/TVT.2018.2808359
• Institution: Nanyang Technological University

The recognition of driver's braking intensity is of great importance for advanced control and energy management for electric vehicles. In this paper, the braking intensity is classified into three levels based on novel hybrid unsupervised and supervised learning methods. First, instead of selec...