Driver-Centric Velocity Prediction With Multidimensional Fuzzy Granulation

被引：3

作者：

Li, Ji ^{[1
]}

Zhou, Quan ^{[1
]}

He, Xu ^{[1
]}

Xu, Hongming ^{[1
]}

机构：

[1] Univ Birmingham, Dept Mech Engn, Birmingham B15 2TT, England

来源：

IEEE-CAA JOURNAL OF AUTOMATICA SINICA | 2023年 / 10卷 / 02期

基金：

英国工程与自然科学研究理事会;

关键词：

Vehicles; Behavioral sciences; Predictive models; Support vector machines; Prediction algorithms; Markov processes; Fuels; DRIVING STYLE; HYBRID; CLASSIFICATION;

D O I：

10.1109/JAS.2022.105998

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Dear Editor, This letter deals with a real-world problem regarding chaotic time series prediction, where a driver-centric velocity prediction model is presented for vehicle intelligent control and advanced driver assistance, i.e., multi-dimension fuzzy predictor. Inspired by fuzzy granulation technology, a finite-state Markov chain (MC) is reinforced to capture probabilities of the transitions between velocity and acceleration and present signals that vary in a continuous range. The predictability of the multi-dimensional fuzzy predictor is examined by comparing two existing MC-based predictors over the two laboratory cycles and one virtual driving cycle, both of which have high accuracy.

引用

页码：547 / 549

页数：3

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