Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.764873
Title: Clustering classification and human perception of automative steering wheel transient vibrations
Author: Mohd Yusoff, Sabariah
ISNI:       0000 0004 7658 265X
Awarding Body: Brunel University London
Current Institution: Brunel University
Date of Award: 2017
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Abstract:
In the 21st century, the proliferation of steer-by-wire systems has become a central issue in the automobile industry. With such systems there is often an objective to minimise vibrations on the steering wheel to increase driver comfort. Nevertheless, steering wheel vibration is also recognised as an important medium that assists drivers in judging the vehicle's subsystems dynamics as well as to indicate important information such as the presence of danger. This has led to studies of the possible role of vibrational stimuli towards informing drivers of environment conditions such as road surface types. Numerous prior studies were done to identify how characteristics of steering wheel vibrational stimuli might influence driver road surface detection which suggested that there is no single, optimal, acceleration gain that could improve the detection of all road surface types. There is currently a lack of studies on the characteristics of transient vibrations of steering wheel as appear to be an important source of information to the driver road surface detection. Therefore, this study is design to identify the similarity characteristics of transient vibrations for answering the main research question: "What are the time-domain features of transient vibrations that can optimise driver road surface detection?" This study starts by critically reviewing the existing principles of transient vibrations detection to ensure that the identified transient vibrations from original steering wheel vibrations satisfy with the definition of transient vibrations. The study continues by performing the experimental activities to identify the optimal measurement signal for both identification process of transient vibrations and driver road surface detection without taking for granted the basic measurement of signal processing. The studies then identify the similarity of transient vibrations according to their time-domain features. The studies done by performing the high-dimensional reduction techniques associated with clustering methods. Result suggests that the time-domain features of transient vibrations that can optimise driver road surface detection were found to consist of duration (Δt), amplitude (m/s2), energy (r.m.s) and Kurtosis.
Supervisor: Giacomin, J. ; Ajovalasit, M. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.764873  DOI: Not available
Keywords: Vibration perception ; Time-domain ; Frequency distribution ; k-means ; t-SNE algorithm
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