• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

智能道路用于自动驾驶事故检测和预警。

Smart Roads for Autonomous Accident Detection and Warnings.

机构信息

Information and Communication Engineering Department, Yeungnam University, Gyeongsan 38541, Korea.

Department of Computer Science, Federal Urdu University of Arts, Science & Technology, Islamabad 45570, Pakistan.

出版信息

Sensors (Basel). 2022 Mar 8;22(6):2077. doi: 10.3390/s22062077.

DOI:10.3390/s22062077
PMID:35336248
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8953218/
Abstract

An increasing number of vehicles on the roads increases the risk of accidents. In bad weather (e.g., heavy rainfall, strong winds, storms, and fog), this risk almost doubles due to bad visibility as well as road conditions. If an accident happens, especially in bad weather, it is important to inform approaching vehicles about it. Otherwise, there might be another accident, i.e., a multiple-vehicle collision (MVC). If the Emergency Operations Center (EOC) is not informed in a timely fashion about the incident, fatalities might increase because they do not receive immediate first aid. Detecting humans or animals would undoubtedly provide us with a better answer for reducing human fatalities in traffic accidents. In this research, an accident alert light and sound (AALS) system is proposed for auto accident detection and alerts with all types of vehicles. No changes are required in non-equipped vehicles (nEVs) and EVs because the system is installed on the roadside. The idea behind this research is to make smart roads (SRs) instead of equipping each vehicle with a separate system. Wireless communication is needed only when an accident is detected. This study is based on different sensors that are used to build SRs to detect accidents. Pre-saved locations are used to reduce the time needed to find the accident's location without the help of a global positioning system (GPS). Additionally, the proposed framework for the AALS also reduces the risk of MVCs.

摘要

道路上的车辆越来越多，增加了发生事故的风险。在恶劣天气（如大雨、强风、暴风雨和雾天）下，由于能见度和道路状况不佳，这种风险几乎增加了一倍。如果发生事故，特别是在恶劣天气下，及时告知接近的车辆是很重要的。否则，可能会发生另一起事故，即多车碰撞（MVC）。如果应急行动中心（EOC）不能及时了解事故情况，可能会增加死亡人数，因为他们无法立即获得急救。检测到人类或动物无疑会为减少交通事故中的人员死亡提供更好的答案。在这项研究中，提出了一种用于自动事故检测和警报的事故警示灯和声音（AALS）系统，适用于各种类型的车辆。由于系统安装在路边，因此无需对非配备车辆（nEV）和电动汽车（EV）进行任何更改。这项研究的背后的想法是建造智能道路（SR），而不是为每辆车配备单独的系统。只有在检测到事故时才需要进行无线通信。这项研究基于不同的传感器，用于构建 SR 以检测事故。使用预存储的位置来减少在没有全球定位系统（GPS）帮助的情况下找到事故位置所需的时间。此外，所提出的 AALS 框架还降低了 MVC 的风险。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc99/8953218/d1a3c1997cc1/sensors-22-02077-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc99/8953218/43b017741c1f/sensors-22-02077-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc99/8953218/941e8be62e81/sensors-22-02077-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc99/8953218/5e7162817f7f/sensors-22-02077-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc99/8953218/65799ec62357/sensors-22-02077-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc99/8953218/eb4fcdde7bfb/sensors-22-02077-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc99/8953218/ff35517847fa/sensors-22-02077-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc99/8953218/0fd69339a025/sensors-22-02077-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc99/8953218/6d71edc7f34f/sensors-22-02077-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc99/8953218/7f449268bc41/sensors-22-02077-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc99/8953218/22bf82fa0f4d/sensors-22-02077-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc99/8953218/bf3ed49cd5ed/sensors-22-02077-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc99/8953218/e237df7ceebf/sensors-22-02077-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc99/8953218/5925072a3392/sensors-22-02077-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc99/8953218/472861adca94/sensors-22-02077-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc99/8953218/d1a3c1997cc1/sensors-22-02077-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc99/8953218/43b017741c1f/sensors-22-02077-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc99/8953218/941e8be62e81/sensors-22-02077-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc99/8953218/5e7162817f7f/sensors-22-02077-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc99/8953218/65799ec62357/sensors-22-02077-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc99/8953218/eb4fcdde7bfb/sensors-22-02077-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc99/8953218/ff35517847fa/sensors-22-02077-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc99/8953218/0fd69339a025/sensors-22-02077-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc99/8953218/6d71edc7f34f/sensors-22-02077-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc99/8953218/7f449268bc41/sensors-22-02077-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc99/8953218/22bf82fa0f4d/sensors-22-02077-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc99/8953218/bf3ed49cd5ed/sensors-22-02077-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc99/8953218/e237df7ceebf/sensors-22-02077-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc99/8953218/5925072a3392/sensors-22-02077-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc99/8953218/472861adca94/sensors-22-02077-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fc99/8953218/d1a3c1997cc1/sensors-22-02077-g015.jpg

相似文献

1
Smart Roads for Autonomous Accident Detection and Warnings.智能道路用于自动驾驶事故检测和预警。
Sensors (Basel). 2022 Mar 8;22(6):2077. doi: 10.3390/s22062077.
2
Analysis on alteration of road traffic casualties in western China from multi-department data in recent decade.近十年中国西部地区多部门道路交通伤害变化分析
Front Public Health. 2022 Nov 10;10:972948. doi: 10.3389/fpubh.2022.972948. eCollection 2022.
3
Accident risk of road and weather conditions on different road types.不同类型道路的路况和天气条件下的事故风险。
Accid Anal Prev. 2019 Jan;122:181-188. doi: 10.1016/j.aap.2018.10.014. Epub 2018 Oct 29.
4
Risk factors associated with traffic violations and accident severity in China.与中国交通违法行为及事故严重程度相关的风险因素。
Accid Anal Prev. 2013 Oct;59:18-25. doi: 10.1016/j.aap.2013.05.004. Epub 2013 May 15.
5
The potential of vehicle and road infrastructure interventions in fatal bicyclist accidents on Swedish roads-What can in-depth studies tell us?车辆和道路基础设施干预在瑞典道路上致命自行车事故中的潜力——深入研究能告诉我们什么？
Traffic Inj Prev. 2019;20(sup1):S7-S12. doi: 10.1080/15389588.2019.1610171.
6
The impact of temperature, rainfall, and health worker density index on road traffic fatalities in Pakistan.温度、降雨和卫生工作者密度指数对巴基斯坦道路交通事故死亡人数的影响。
Environ Sci Pollut Res Int. 2020 Jun;27(16):19510-19529. doi: 10.1007/s11356-020-08233-1. Epub 2020 Mar 25.
7
Situational characteristics of fatal pedestrian accidents involving vehicles traveling at low speeds in Japan.日本低速车辆致人死亡的行人事故的情境特征。
Traffic Inj Prev. 2019;20(sup1):S1-S6. doi: 10.1080/15389588.2019.1587166.
8
Advanced e-Call Support Based on Non-Intrusive Driver Condition Monitoring for Connected and Autonomous Vehicles.基于非侵入式驾驶员状态监测的联网自动驾驶汽车高级电子路侧求助系统。
Sensors (Basel). 2021 Dec 10;21(24):8272. doi: 10.3390/s21248272.
9
Agricultural vehicles and rural road safety: tackling a persistent problem.农业车辆与农村道路安全：解决一个长期存在的问题。
Traffic Inj Prev. 2014;15(1):94-101. doi: 10.1080/15389588.2013.789135.
10
All-terrain vehicle fatalities on paved roads, unpaved roads, and off-road: Evidence for informed roadway safety warnings and legislation.全地形车在铺砌道路、未铺砌道路及越野区域的死亡事故：关于提供充分道路安全警示及立法的证据
Traffic Inj Prev. 2016 May 18;17(4):406-12. doi: 10.1080/15389588.2015.1057280. Epub 2015 Jun 11.

引用本文的文献

1
Sybil Attack-Resistant Blockchain-Based Proof-of-Location Mechanism with Privacy Protection in VANET.车载自组网中具有隐私保护的基于区块链的抗女巫攻击位置证明机制
Sensors (Basel). 2024 Dec 20;24(24):8140. doi: 10.3390/s24248140.
2
Machine learning based IoT system for secure traffic management and accident detection in smart cities.用于智慧城市中安全交通管理和事故检测的基于机器学习的物联网系统。
PeerJ Comput Sci. 2023 Mar 8;9:e1259. doi: 10.7717/peerj-cs.1259. eCollection 2023.
3
Increasing Vehicular Visible Light Communications Range Based on LED Current Overdriving and Variable Pulse Position Modulation: Concept and Experimental Validation.

本文引用的文献

1
A Performance Benchmark for Dedicated Short-Range Communications and LTE-Based Cellular-V2X in the Context of Vehicle-to-Infrastructure Communication and Urban Scenarios.面向车对基础设施通信和城市场景的专用短程通信和基于 LTE 的蜂窝车对车通信的性能基准测试。
Sensors (Basel). 2021 Jul 28;21(15):5095. doi: 10.3390/s21155095.
2
Efficient and Stable Routing Algorithm Based on User Mobility and Node Density in Urban Vehicular Network.基于城市车辆网络中用户移动性和节点密度的高效稳定路由算法
PLoS One. 2016 Nov 17;11(11):e0165966. doi: 10.1371/journal.pone.0165966. eCollection 2016.
基于 LED 电流过驱动和可变脉冲位置调制的提高车载可见光通信距离的方法：概念和实验验证。
Sensors (Basel). 2023 Mar 31;23(7):3656. doi: 10.3390/s23073656.
4
An Automatic Incident Detection Method for a Vehicle-to-Infrastructure Communication Environment: Case Study of Interstate 64 in Missouri.一种车对基础设施通信环境的自动事故检测方法：以密苏里州 I-64 州际公路为例。
Sensors (Basel). 2022 Nov 26;22(23):9197. doi: 10.3390/s22239197.