运用机器学习分析驾驶技能的全曲线和曲线分段情况

N. P. Chandrasiri, Kazunari Nawa, Akira Ishii, Shuguang Li, Shigeyuki Yamabe, T. Hirasawa, Yoichi Sato, Y. Suda, Takeshi Matsumura, Koji Taguchi
{"title":"运用机器学习分析驾驶技能的全曲线和曲线分段情况","authors":"N. P. Chandrasiri, Kazunari Nawa, Akira Ishii, Shuguang Li, Shigeyuki Yamabe, T. Hirasawa, Yoichi Sato, Y. Suda, Takeshi Matsumura, Koji Taguchi","doi":"10.1109/ITST.2012.6425238","DOIUrl":null,"url":null,"abstract":"Analysis of driving skill/driver state can be used in building driver support and infotainment systems that can be adapted to individual needs of a driver. In this paper we present a machine learning approach to analyzing driving maneuver skills of a driver that covers both longitudinal and lateral controls. The concept is to learn a driver model from sensor data that are related to driving environment, driving behavior and vehicle response. Once the model is built, driving skills of an unknown run can be classified automatically. In this paper, we demonstrate the feasibility of the proposed method for driving skill analysis based on a driving simulator experiment in a curve driving scene for both the full curve and curve segmented cases.","PeriodicalId":143706,"journal":{"name":"2012 12th International Conference on ITS Telecommunications","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":"{\"title\":\"Driving skill analysis using machine learning The full curve and curve segmented cases\",\"authors\":\"N. P. Chandrasiri, Kazunari Nawa, Akira Ishii, Shuguang Li, Shigeyuki Yamabe, T. Hirasawa, Yoichi Sato, Y. Suda, Takeshi Matsumura, Koji Taguchi\",\"doi\":\"10.1109/ITST.2012.6425238\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Analysis of driving skill/driver state can be used in building driver support and infotainment systems that can be adapted to individual needs of a driver. In this paper we present a machine learning approach to analyzing driving maneuver skills of a driver that covers both longitudinal and lateral controls. The concept is to learn a driver model from sensor data that are related to driving environment, driving behavior and vehicle response. Once the model is built, driving skills of an unknown run can be classified automatically. In this paper, we demonstrate the feasibility of the proposed method for driving skill analysis based on a driving simulator experiment in a curve driving scene for both the full curve and curve segmented cases.\",\"PeriodicalId\":143706,\"journal\":{\"name\":\"2012 12th International Conference on ITS Telecommunications\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"14\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 12th International Conference on ITS Telecommunications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITST.2012.6425238\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 12th International Conference on ITS Telecommunications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITST.2012.6425238","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 14

摘要

对驾驶技能/驾驶员状态的分析可以用于构建驾驶员支持和信息娱乐系统,这些系统可以适应驾驶员的个人需求。在本文中,我们提出了一种机器学习方法来分析驾驶员的驾驶机动技能,包括纵向和横向控制。其概念是从与驾驶环境、驾驶行为和车辆响应相关的传感器数据中学习驾驶员模型。建立模型后,可以对未知路段的驾驶技能进行自动分类。在本文中,我们通过驾驶模拟器实验,在曲线驾驶场景中对全曲线和曲线分段情况进行了驾驶技能分析,验证了该方法的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Driving skill analysis using machine learning The full curve and curve segmented cases
Analysis of driving skill/driver state can be used in building driver support and infotainment systems that can be adapted to individual needs of a driver. In this paper we present a machine learning approach to analyzing driving maneuver skills of a driver that covers both longitudinal and lateral controls. The concept is to learn a driver model from sensor data that are related to driving environment, driving behavior and vehicle response. Once the model is built, driving skills of an unknown run can be classified automatically. In this paper, we demonstrate the feasibility of the proposed method for driving skill analysis based on a driving simulator experiment in a curve driving scene for both the full curve and curve segmented cases.
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:481959085
Book学术官方微信