Roughness-induced vehicle energy dissipation from crowdsourced smartphone measurements through random vibration theory

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Meshkat Botshekan, J. Roxon, Athikom Wanichkul, Theemathas Chirananthavat, J. Chamoun, Malik Ziq, Bader Anini, Naseem A. Daher, Abdalkarim Awad, Wasel T. Ghanem, M. Tootkaboni, A. Louhghalam, F. Ulm
{"title":"Roughness-induced vehicle energy dissipation from crowdsourced smartphone measurements through random vibration theory","authors":"Meshkat Botshekan, J. Roxon, Athikom Wanichkul, Theemathas Chirananthavat, J. Chamoun, Malik Ziq, Bader Anini, Naseem A. Daher, Abdalkarim Awad, Wasel T. Ghanem, M. Tootkaboni, A. Louhghalam, F. Ulm","doi":"10.1017/dce.2020.17","DOIUrl":null,"url":null,"abstract":"Abstract We propose, calibrate, and validate a crowdsourced approach for estimating power spectral density (PSD) of road roughness based on an inverse analysis of vertical acceleration measured by a smartphone mounted in an unknown position in a vehicle. Built upon random vibration analysis of a half-car mechanistic model of roughness-induced pavement–vehicle interaction, the inverse analysis employs an L2 norm regularization to estimate ride quality metrics, such as the widely used International Roughness Index, from the acceleration PSD. Evoking the fluctuation–dissipation theorem of statistical physics, the inverse framework estimates the half-car dynamic vehicle properties and related excess fuel consumption. The method is validated against (a) laser-measured road roughness data for both inner city and highway road conditions and (b) road roughness data for the state of California. We also show that the phone position in the vehicle only marginally affects road roughness predictions, an important condition for crowdsourced capabilities of the proposed approach.","PeriodicalId":34169,"journal":{"name":"DataCentric Engineering","volume":" ","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2020-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/dce.2020.17","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"DataCentric Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1017/dce.2020.17","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 3

Abstract

Abstract We propose, calibrate, and validate a crowdsourced approach for estimating power spectral density (PSD) of road roughness based on an inverse analysis of vertical acceleration measured by a smartphone mounted in an unknown position in a vehicle. Built upon random vibration analysis of a half-car mechanistic model of roughness-induced pavement–vehicle interaction, the inverse analysis employs an L2 norm regularization to estimate ride quality metrics, such as the widely used International Roughness Index, from the acceleration PSD. Evoking the fluctuation–dissipation theorem of statistical physics, the inverse framework estimates the half-car dynamic vehicle properties and related excess fuel consumption. The method is validated against (a) laser-measured road roughness data for both inner city and highway road conditions and (b) road roughness data for the state of California. We also show that the phone position in the vehicle only marginally affects road roughness predictions, an important condition for crowdsourced capabilities of the proposed approach.
通过随机振动理论众包智能手机测量粗糙度引起的车辆能量耗散
摘要我们提出、校准并验证了一种众包方法,用于估计道路粗糙度的功率谱密度(PSD),该方法基于安装在车辆未知位置的智能手机测量的垂直加速度的逆分析。基于粗糙度引起的路面-车辆相互作用的半车机械模型的随机振动分析,逆分析采用L2范数正则化来根据加速度PSD估计行驶质量指标,如广泛使用的国际粗糙度指数。逆框架唤起了统计物理学的波动-耗散定理,估计了半车动态车辆的特性和相关的超额油耗。该方法根据(a)内城和公路路况的激光测量道路粗糙度数据和(b)加利福尼亚州的道路粗糙度进行了验证。我们还表明,手机在车辆中的位置仅对道路粗糙度预测产生轻微影响,这是所提出方法众包能力的重要条件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信