从连续的自然驾驶数据中提取信息:示例应用程序

Miguel A. Perez, Zachary R. Doerzaph, C. Gaylord, J. Hankey
{"title":"从连续的自然驾驶数据中提取信息:示例应用程序","authors":"Miguel A. Perez, Zachary R. Doerzaph, C. Gaylord, J. Hankey","doi":"10.1109/IVS.2010.5547964","DOIUrl":null,"url":null,"abstract":"The technology and tools used for naturalistic driving data collection have evolved greatly in recent years. Data collection efforts that required a trunk full of equipment and days of installation can now be achieved with data acquisition systems that are about the size of a deck of cards and can be installed in minutes. This evolution has made possible large-scale driving data collection efforts, such as the upcoming Second Safety Highway Research Program Naturalistic Driving Study (SHRP2 NDS). Data from naturalistic studies allow for an unparalleled breadth and depth of driver behavior analysis that goes beyond the quantification and description of driver distraction into a deeper understanding of how drivers interact with their vehicles. This paper describes key aspects of how such studies are designed and executed, and provides some examples of how common types of data are extracted from these naturalistic driving datasets. Specifically, the use of RADAR and speed data are discussed in detail. In addition, a sample architecture for the storage of and access to these vast quantities of driving data and video is provided. Naturalistic driving data have allowed for a transformation in the understanding of driver behavior and, as datasets are expanded to include diverse populations, they will help researchers and automotive engineers in developing novel ways to mitigate and prevent vehicular crashes and their consequences.","PeriodicalId":123266,"journal":{"name":"2010 IEEE Intelligent Vehicles Symposium","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Extracting information from continuous naturalistic driving data: sample applications\",\"authors\":\"Miguel A. Perez, Zachary R. Doerzaph, C. Gaylord, J. Hankey\",\"doi\":\"10.1109/IVS.2010.5547964\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The technology and tools used for naturalistic driving data collection have evolved greatly in recent years. Data collection efforts that required a trunk full of equipment and days of installation can now be achieved with data acquisition systems that are about the size of a deck of cards and can be installed in minutes. This evolution has made possible large-scale driving data collection efforts, such as the upcoming Second Safety Highway Research Program Naturalistic Driving Study (SHRP2 NDS). Data from naturalistic studies allow for an unparalleled breadth and depth of driver behavior analysis that goes beyond the quantification and description of driver distraction into a deeper understanding of how drivers interact with their vehicles. This paper describes key aspects of how such studies are designed and executed, and provides some examples of how common types of data are extracted from these naturalistic driving datasets. Specifically, the use of RADAR and speed data are discussed in detail. In addition, a sample architecture for the storage of and access to these vast quantities of driving data and video is provided. Naturalistic driving data have allowed for a transformation in the understanding of driver behavior and, as datasets are expanded to include diverse populations, they will help researchers and automotive engineers in developing novel ways to mitigate and prevent vehicular crashes and their consequences.\",\"PeriodicalId\":123266,\"journal\":{\"name\":\"2010 IEEE Intelligent Vehicles Symposium\",\"volume\":\"31 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-06-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 IEEE Intelligent Vehicles Symposium\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IVS.2010.5547964\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE Intelligent Vehicles Symposium","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IVS.2010.5547964","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

摘要

近年来,用于自然驾驶数据收集的技术和工具有了很大的发展。以前需要一整箱设备和数天的安装时间才能完成的数据采集工作,现在可以通过一副扑克牌大小的数据采集系统来完成,并且可以在几分钟内完成安装。这一技术进步使得大规模驾驶数据收集工作成为可能,例如即将开展的第二安全公路研究项目自然驾驶研究(SHRP2 NDS)。来自自然主义研究的数据提供了无与伦比的广度和深度的驾驶员行为分析,超越了驾驶员分心的量化和描述,更深入地了解了驾驶员与车辆的互动方式。本文描述了如何设计和执行此类研究的关键方面,并提供了如何从这些自然驱动数据集中提取常见类型数据的一些示例。具体来说,详细讨论了雷达和航速数据的使用。此外,还提供了一个用于存储和访问这些大量驾驶数据和视频的示例架构。自然驾驶数据使得对驾驶员行为的理解发生了转变,随着数据集的扩展,包括不同的人群,它们将帮助研究人员和汽车工程师开发新的方法来减轻和预防车辆碰撞及其后果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Extracting information from continuous naturalistic driving data: sample applications
The technology and tools used for naturalistic driving data collection have evolved greatly in recent years. Data collection efforts that required a trunk full of equipment and days of installation can now be achieved with data acquisition systems that are about the size of a deck of cards and can be installed in minutes. This evolution has made possible large-scale driving data collection efforts, such as the upcoming Second Safety Highway Research Program Naturalistic Driving Study (SHRP2 NDS). Data from naturalistic studies allow for an unparalleled breadth and depth of driver behavior analysis that goes beyond the quantification and description of driver distraction into a deeper understanding of how drivers interact with their vehicles. This paper describes key aspects of how such studies are designed and executed, and provides some examples of how common types of data are extracted from these naturalistic driving datasets. Specifically, the use of RADAR and speed data are discussed in detail. In addition, a sample architecture for the storage of and access to these vast quantities of driving data and video is provided. Naturalistic driving data have allowed for a transformation in the understanding of driver behavior and, as datasets are expanded to include diverse populations, they will help researchers and automotive engineers in developing novel ways to mitigate and prevent vehicular crashes and their consequences.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
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:604180095
Book学术官方微信