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 selecting threshold for each braking intensity level manually, an unsupervised Gaussian mixture model is used to cluster the braking events automatically with brake pressure. Then, a supervised Random Forest model is trained to classify the correct braking intensity levels with the state signals of vehicle and powertrain. To obtain a more efficient classifier, critical features are analyzed and selected. Moreover, beyond the acquisition of discrete braking intensity level, a novel continuous observation method is proposed based on artificial neural networks to quantitative analyze and recognize the brake intensity using the prior determined features of vehicle states. Experimental data are collected in an electric vehicle under real-world driving scenarios. Finally, the classification and regression results of the proposed methods are evaluated and discussed. The results demonstrate the feasibility and accuracy of the proposed hybrid learning methods for braking intensity classification and quantitative recognition with various deceleration scenarios.