Recognition of Driver Braking Intensity of EHB System Using a Hybrid Learning Approach

Accurate recognition of driver braking intensity is of great importance for intelligent braking system. In this paper, the braking intensity is classified into four clusters based on an unsupervised Gaussian mixture model (GMM). Then, the architecture of an adaptive-network-based fuzzy inference sys...

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Bibliographic Details
Main Authors: Yang, Haohan, Kaku, Chuyo, Yu, Fan
Format: Conference Proceeding
Language:English
Subjects:
Brakes
Computer architecture
Estimation
Fuzzy logic
Gaussian distribution
Training
Vehicles
Online Access:Request full text
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Summary:Accurate recognition of driver braking intensity is of great importance for intelligent braking system. In this paper, the braking intensity is classified into four clusters based on an unsupervised Gaussian mixture model (GMM). Then, the architecture of an adaptive-network-based fuzzy inference system (ANFIS) is proposed for braking intensity prediction. A batch learning rule that combines the recursive least squares and gradient descent method used for training ANFIS is adopted to improve the generalization capability. The training data are collected from a hybrid vehicle under real driving conditions. In addition, co-simulation with the software of MATLAB/Simulink and Hardware-in-the-Loop (HiL) tests for an Electronic-Hydraulic Brake (EHB) system are carried out. In comparison to other typical learning methods, the simulation and experimental results demonstrate the effectiveness and accuracy of the proposed hybrid learning approach for braking intensity recognition in different braking scenarios.
ISSN:2642-7214
DOI:10.1109/IV47402.2020.9304805