基于特定道路曲率点的车辆加速度预测

ICINCO-RA Pub Date : 1900-01-01 DOI:10.5220/0002173401470152
A. Vidugiriene, A. Demčenko, M. Tamosiunaite
{"title":"基于特定道路曲率点的车辆加速度预测","authors":"A. Vidugiriene, A. Demčenko, M. Tamosiunaite","doi":"10.5220/0002173401470152","DOIUrl":null,"url":null,"abstract":"In the work vehicle acceleration prediction issue is discussed. Three types of parameters are used for prediction system input: CAN-bus parameters - speed and curvature, derived speed parameters and newly offered specific curve point parameters, denoting changes in a curve. The real road data was used for predictions. Road curvature segments were divided into single and S-type curves. Acceleration was predicted using artificial neural networks and look-up table. The look-up table method showed the best results with newly offered specific curve parameters.","PeriodicalId":302311,"journal":{"name":"ICINCO-RA","volume":"48 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Vehicle Acceleration Prediction using Specific Road Curvature Points\",\"authors\":\"A. Vidugiriene, A. Demčenko, M. Tamosiunaite\",\"doi\":\"10.5220/0002173401470152\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the work vehicle acceleration prediction issue is discussed. Three types of parameters are used for prediction system input: CAN-bus parameters - speed and curvature, derived speed parameters and newly offered specific curve point parameters, denoting changes in a curve. The real road data was used for predictions. Road curvature segments were divided into single and S-type curves. Acceleration was predicted using artificial neural networks and look-up table. The look-up table method showed the best results with newly offered specific curve parameters.\",\"PeriodicalId\":302311,\"journal\":{\"name\":\"ICINCO-RA\",\"volume\":\"48 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ICINCO-RA\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5220/0002173401470152\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ICINCO-RA","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5220/0002173401470152","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

讨论了工作车辆加速度预测问题。三种类型的参数用于预测系统输入:can总线参数-速度和曲率,导出的速度参数和新提供的特定曲线点参数,表示曲线的变化。真实的道路数据被用于预测。道路曲率段分为单曲线段和s型曲线段。利用人工神经网络和查找表对加速度进行了预测。在新提供的特定曲线参数下,查表法得到的结果最好。
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
Vehicle Acceleration Prediction using Specific Road Curvature Points
In the work vehicle acceleration prediction issue is discussed. Three types of parameters are used for prediction system input: CAN-bus parameters - speed and curvature, derived speed parameters and newly offered specific curve point parameters, denoting changes in a curve. The real road data was used for predictions. Road curvature segments were divided into single and S-type curves. Acceleration was predicted using artificial neural networks and look-up table. The look-up table method showed the best results with newly offered specific curve parameters.
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
Calibration Aspects of Multiple Line-scan Vision System Application for Planar Objects Inspection Automatic Generation of Executable Code for a Robot Cell using UPNP and XIRP Kamanbaré - a tree-climbing biomimetic robotic platform for environmental research Monte carlo localization in highly symmetric environments The tele-echography medical robot Otelo2 - teleoperated with a multi level architecture using trinomial protocol
×
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