Apply Image Identification to Improve the Localization of the Self-Driving Vehicles

Shaohui Liu, Shih-Yen Huang
{"title":"Apply Image Identification to Improve the Localization of the Self-Driving Vehicles","authors":"Shaohui Liu, Shih-Yen Huang","doi":"10.1109/SNPD51163.2021.9704956","DOIUrl":null,"url":null,"abstract":"Location failure is dangerous for self-driving vehicles. Adaptive Monte Carlo Localization (AMCL)[1] provides wrong coordinates to the self-driving controller in some specific conditions. This paper proposed a scheme to solve this problem. This scheme provides a reference location to AMCL, which could exactly give coordinates to the self-driving controller. The experiment results showed that this reference location could improve the performance of AMCL to provide precise coordinates to the self-driving controller. In addition, to provide reference location to AMCL, this proposed scheme applied Convolutional Neural Network (CNN)[2] to identify the specific scenery front the vehicle. Accordingly, detect particular views will be another challenge for self-driving vehicles.","PeriodicalId":235370,"journal":{"name":"2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SNPD51163.2021.9704956","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Location failure is dangerous for self-driving vehicles. Adaptive Monte Carlo Localization (AMCL)[1] provides wrong coordinates to the self-driving controller in some specific conditions. This paper proposed a scheme to solve this problem. This scheme provides a reference location to AMCL, which could exactly give coordinates to the self-driving controller. The experiment results showed that this reference location could improve the performance of AMCL to provide precise coordinates to the self-driving controller. In addition, to provide reference location to AMCL, this proposed scheme applied Convolutional Neural Network (CNN)[2] to identify the specific scenery front the vehicle. Accordingly, detect particular views will be another challenge for self-driving vehicles.
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
应用图像识别技术提高自动驾驶车辆的定位
定位失败对自动驾驶汽车来说是很危险的。自适应蒙特卡罗定位(AMCL)[1]在某些特定条件下会向自驾车控制器提供错误的坐标。本文提出了一种解决这一问题的方案。该方案为AMCL提供了一个参考位置,可以准确地给出自驾车控制器的坐标。实验结果表明,该参考位置可以提高AMCL的性能,为自驾车控制器提供精确的坐标。此外,为了给AMCL提供参考位置,本方案采用卷积神经网络(CNN)[2]来识别车辆前方的特定景物。因此,检测特定视角将是自动驾驶汽车面临的另一个挑战。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Quantum Annealing Approach for the Optimal Real-time Traffic Control using QUBO How to Enlighten Novice Users on Behavior of Machine Learning Models? Keynote Address: Deep Learning Networks for Medical Image Analysis: Its Past, Future, and Issues Web-based systems for inventory control in organizations: A Systematic Review Geometrical Schemes as Probabilistic and Entropic Tools to Estimate Duration and Peaks of Pandemic Waves
×
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