Controlling Sensor Data Dissemination Method for Collective Perception in VANET

Kaito Furukawa, M. Takai, S. Ishihara
{"title":"Controlling Sensor Data Dissemination Method for Collective Perception in VANET","authors":"Kaito Furukawa, M. Takai, S. Ishihara","doi":"10.1109/PERCOMW.2019.8730601","DOIUrl":null,"url":null,"abstract":"Vehicles can expand its own perceptual range about road traffic by collective perception technique for sharing sensor data about objects in vicinity among the neighbors by using Vehicle-to-Vehicle communication. In high vehicle density, however, packet collisions and the hidden terminal problem make it difficult to deliver messages containing the sensor data. To ensure delivering the useful sensor data for avoiding collision accidents, we proposed the strategy of a method to control the transmission frequency of sensor data based on the positional relationship of vehicles and the road structure in order to improve the surrounding awareness of vehicles in previous our work. In this paper, based on the strategy, we propose the detail of the strategy, the scheme for automatically select vehicles with a high probability to broadcast sensor data, method and evaluate the effectiveness of the method through simulations compared with a related work. This method is effective for avoiding collision accidents because vehicles can perceive the presence of other vehicles while reducing radio traffic.","PeriodicalId":437017,"journal":{"name":"2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PERCOMW.2019.8730601","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

Abstract

Vehicles can expand its own perceptual range about road traffic by collective perception technique for sharing sensor data about objects in vicinity among the neighbors by using Vehicle-to-Vehicle communication. In high vehicle density, however, packet collisions and the hidden terminal problem make it difficult to deliver messages containing the sensor data. To ensure delivering the useful sensor data for avoiding collision accidents, we proposed the strategy of a method to control the transmission frequency of sensor data based on the positional relationship of vehicles and the road structure in order to improve the surrounding awareness of vehicles in previous our work. In this paper, based on the strategy, we propose the detail of the strategy, the scheme for automatically select vehicles with a high probability to broadcast sensor data, method and evaluate the effectiveness of the method through simulations compared with a related work. This method is effective for avoiding collision accidents because vehicles can perceive the presence of other vehicles while reducing radio traffic.
VANET集体感知控制传感器数据传播方法
车辆可以通过集体感知技术扩大自身对道路交通的感知范围,通过车与车之间的通信,在邻居之间共享附近物体的传感器数据。然而,在高车辆密度下,数据包碰撞和隐藏终端问题使得包含传感器数据的消息难以传递。为了保证提供有用的传感器数据以避免碰撞事故,我们在之前的工作中提出了一种基于车辆位置关系和道路结构控制传感器数据传输频率的方法,以提高车辆的周围感知。本文基于该策略,提出了该策略的细节,自动选择高概率车辆广播传感器数据的方案,方法,并通过仿真与相关工作进行比较,评价了该方法的有效性。这种方法可以有效地避免碰撞事故,因为车辆可以感知其他车辆的存在,同时减少无线电通信量。
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 求助全文
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