在驾驶自动驾驶汽车时,识别各种道路事件中不适的来源

Guy Cohen-Lazry, Ariel Telpaz, A. Degani, T. Oron-Gilad
{"title":"在驾驶自动驾驶汽车时,识别各种道路事件中不适的来源","authors":"Guy Cohen-Lazry, Ariel Telpaz, A. Degani, T. Oron-Gilad","doi":"10.1109/ICHMS49158.2020.9209461","DOIUrl":null,"url":null,"abstract":"The present study aimed at exploring the factors that induce discomfort when riding automated vehicles in various driving events. Using a Wizard-of-Oz design, participants were driven in what appeared to be a fully automated (level 5) vehicle. An analysis of participants’ reactions and verbal utterances revealed six specific sources of discomfort (Vehicle’s speed, Crossing Traffic, Pedestrians, Road Geometry, Road Markings and On-coming Traffic). These discomfort reactions were then cross-referenced to the road events they were associated with. The results suggest that there is a different set of discomfort sources associated with each unique road event. Overall, we identified seven distinct categories of road events that could cover the entire experimental route (Crosswalks, Roundabouts, Straight Intersections, Left-Turns with On-coming Traffic, Left-Turns without On-coming Traffic, Right turns with Right-of-way and Right turns without Right-of-way). From a design perspective, these findings can empower the development of interventions to reduce discomfort in automated vehicles.","PeriodicalId":132917,"journal":{"name":"2020 IEEE International Conference on Human-Machine Systems (ICHMS)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Identifying Sources of Discomfort in Various Road Events While Riding Automated Vehicles\",\"authors\":\"Guy Cohen-Lazry, Ariel Telpaz, A. Degani, T. Oron-Gilad\",\"doi\":\"10.1109/ICHMS49158.2020.9209461\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The present study aimed at exploring the factors that induce discomfort when riding automated vehicles in various driving events. Using a Wizard-of-Oz design, participants were driven in what appeared to be a fully automated (level 5) vehicle. An analysis of participants’ reactions and verbal utterances revealed six specific sources of discomfort (Vehicle’s speed, Crossing Traffic, Pedestrians, Road Geometry, Road Markings and On-coming Traffic). These discomfort reactions were then cross-referenced to the road events they were associated with. The results suggest that there is a different set of discomfort sources associated with each unique road event. Overall, we identified seven distinct categories of road events that could cover the entire experimental route (Crosswalks, Roundabouts, Straight Intersections, Left-Turns with On-coming Traffic, Left-Turns without On-coming Traffic, Right turns with Right-of-way and Right turns without Right-of-way). From a design perspective, these findings can empower the development of interventions to reduce discomfort in automated vehicles.\",\"PeriodicalId\":132917,\"journal\":{\"name\":\"2020 IEEE International Conference on Human-Machine Systems (ICHMS)\",\"volume\":\"66 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE International Conference on Human-Machine Systems (ICHMS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICHMS49158.2020.9209461\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE International Conference on Human-Machine Systems (ICHMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICHMS49158.2020.9209461","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

本研究旨在探讨在各种驾驶活动中驾驶自动驾驶汽车时引起不适的因素。使用Wizard-of-Oz的设计,参与者被驾驶在一辆完全自动化(5级)的车辆中。对参与者的反应和言语的分析揭示了6个具体的不舒服来源(车速、过路交通、行人、道路几何形状、道路标记和迎面而来的车辆)。然后将这些不适反应与与之相关的道路事件进行交叉对照。结果表明,每个独特的道路事件都有不同的不适来源。总的来说,我们确定了七种不同类别的道路事件,可以覆盖整个实验路线(人行横道,环形交叉路口,直十字路口,左转弯有来车,左转弯没有来车,右转弯有路权和右转弯没有路权)。从设计的角度来看,这些发现可以促进干预措施的发展,以减少自动驾驶汽车的不适。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Identifying Sources of Discomfort in Various Road Events While Riding Automated Vehicles
The present study aimed at exploring the factors that induce discomfort when riding automated vehicles in various driving events. Using a Wizard-of-Oz design, participants were driven in what appeared to be a fully automated (level 5) vehicle. An analysis of participants’ reactions and verbal utterances revealed six specific sources of discomfort (Vehicle’s speed, Crossing Traffic, Pedestrians, Road Geometry, Road Markings and On-coming Traffic). These discomfort reactions were then cross-referenced to the road events they were associated with. The results suggest that there is a different set of discomfort sources associated with each unique road event. Overall, we identified seven distinct categories of road events that could cover the entire experimental route (Crosswalks, Roundabouts, Straight Intersections, Left-Turns with On-coming Traffic, Left-Turns without On-coming Traffic, Right turns with Right-of-way and Right turns without Right-of-way). From a design perspective, these findings can empower the development of interventions to reduce discomfort in automated vehicles.
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
期刊最新文献
Finite Time Sliding Mode Control of Connected Vehicle Platoons Guaranteeing String Stability User detection of threats with different security measures Driver Hazard Response When Processing On-road and In-vehicle Messaging of Non-Safety-Related Information Towards trustworthiness and transparency in social human-robot interaction Collaborative Environmental Monitoring through Teams of Trusted IoT devices
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1