Letter: driver-centric velocity prediction with multidimensional fuzzy granulation

Ji Li; Quan Zhou; Xu He; Hongming Xu

doi:10.1109/JAS.2022.105998

Letter: driver-centric velocity prediction with multidimensional fuzzy granulation

Ji Li, Quan Zhou, Xu He, Hongming Xu

Mechanical Engineering

Research output: Contribution to journal › Letter › peer-review

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Abstract

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.

Original language	English
Article number	9910369
Number of pages	3
Journal	IEEE/CAA Journal of Automatica Sinica
Early online date	4 Oct 2022
DOIs	https://doi.org/10.1109/JAS.2022.105998
Publication status	E-pub ahead of print - 4 Oct 2022

Keywords

Vehicles
Behavioral sciences
Predictive models
Support vector machines
Prediction algorithms
Markov processes
Fuels

ASJC Scopus subject areas

Control and Optimization
Artificial Intelligence
Information Systems
Control and Systems Engineering

Access to Document

10.1109/JAS.2022.105998

LiJ2022Letter
J. Li, Q. Zhou, X. He and H. Xu, "Letter: Driver-centric velocity prediction with multidimensional fuzzy granulation," in IEEE/CAA Journal of Automatica Sinica, doi: 10.1109/JAS.2022.105998. © 2022 by the Chinese Association of Automation. All rights reserved.
Accepted author manuscript, 346 KBLicence: None: All rights reserved

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9910369

Cite this

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title = "Letter: driver-centric velocity prediction with multidimensional fuzzy granulation",

abstract = "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.",

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AU - Li, Ji

AU - Zhou, Quan

AU - He, Xu

AU - Xu, Hongming

PY - 2022/10/4

Y1 - 2022/10/4

N2 - 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.

AB - 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.

KW - Vehicles

KW - Behavioral sciences

KW - Predictive models

KW - Support vector machines

KW - Prediction algorithms

KW - Markov processes

KW - Fuels

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M3 - Letter

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JO - IEEE/CAA Journal of Automatica Sinica

JF - IEEE/CAA Journal of Automatica Sinica

M1 - 9910369

ER -

Letter: driver-centric velocity prediction with multidimensional fuzzy granulation

Abstract

Keywords

ASJC Scopus subject areas

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