认知驾驶数据可视化与驾驶风格迁移

H. Hiraishi
{"title":"认知驾驶数据可视化与驾驶风格迁移","authors":"H. Hiraishi","doi":"10.1109/ICCICC53683.2021.9811337","DOIUrl":null,"url":null,"abstract":"This study proposes a visualization method for imaging cognitive driving data, which records data related to driving operations and drivers’ cognitive states. The visualization yielded an easy understanding of the characteristics of the driving operation and cognitive states. This allows intuitive comparison using images. Some differences between novice and experienced drivers can be visually embossed. Furthermore, the image style transfer algorithm using deep learning can be adopted for driving style transfer by representing the driving data as an image. Therefore, the image of an experienced driver can be used as a style image, and that of a novice driver can be modified by the style of an experienced driver. In this study, some trials of driving style transfer were attempted. Consequently, the driving style of a novice driver can be modified to clear and smooth operations like those of an experienced driver. As for the cognitive state, although a novice driver has always felt stressed up, the style of relaxed driving based on road conditions can be applied.","PeriodicalId":101653,"journal":{"name":"2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Cognitive Driving Data Visualization and Driving Style Transfer\",\"authors\":\"H. Hiraishi\",\"doi\":\"10.1109/ICCICC53683.2021.9811337\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study proposes a visualization method for imaging cognitive driving data, which records data related to driving operations and drivers’ cognitive states. The visualization yielded an easy understanding of the characteristics of the driving operation and cognitive states. This allows intuitive comparison using images. Some differences between novice and experienced drivers can be visually embossed. Furthermore, the image style transfer algorithm using deep learning can be adopted for driving style transfer by representing the driving data as an image. Therefore, the image of an experienced driver can be used as a style image, and that of a novice driver can be modified by the style of an experienced driver. In this study, some trials of driving style transfer were attempted. Consequently, the driving style of a novice driver can be modified to clear and smooth operations like those of an experienced driver. As for the cognitive state, although a novice driver has always felt stressed up, the style of relaxed driving based on road conditions can be applied.\",\"PeriodicalId\":101653,\"journal\":{\"name\":\"2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)\",\"volume\":\"55 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCICC53683.2021.9811337\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE 20th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCICC53683.2021.9811337","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

本研究提出了一种成像认知驾驶数据的可视化方法,记录驾驶操作和驾驶员认知状态的相关数据。可视化使驾驶操作和认知状态的特征易于理解。这允许使用图像进行直观的比较。新手和有经验的司机之间的一些差异可以在视觉上体现出来。此外,通过将驾驶数据表示为图像,可以采用深度学习的图像风格迁移算法进行驾驶风格迁移。因此,一个有经验的司机的形象可以作为一个风格形象,一个新手司机的形象可以通过一个有经验的司机的风格来修改。本研究对驾驶风格迁移进行了一些尝试。因此,新手司机的驾驶风格可以修改为像经验丰富的司机那样清晰流畅的操作。在认知状态上,虽然新手驾驶员总是感到压力很大,但可以根据路况采取放松驾驶的方式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cognitive Driving Data Visualization and Driving Style Transfer
This study proposes a visualization method for imaging cognitive driving data, which records data related to driving operations and drivers’ cognitive states. The visualization yielded an easy understanding of the characteristics of the driving operation and cognitive states. This allows intuitive comparison using images. Some differences between novice and experienced drivers can be visually embossed. Furthermore, the image style transfer algorithm using deep learning can be adopted for driving style transfer by representing the driving data as an image. Therefore, the image of an experienced driver can be used as a style image, and that of a novice driver can be modified by the style of an experienced driver. In this study, some trials of driving style transfer were attempted. Consequently, the driving style of a novice driver can be modified to clear and smooth operations like those of an experienced driver. As for the cognitive state, although a novice driver has always felt stressed up, the style of relaxed driving based on road conditions can be applied.
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