基于并行驾驶操作系统的地下矿山自动驾驶：挑战、框架和案例研究

IF 14.3 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-08-28 DOI:10.1109/TIV.2024.3450609

Bin Tian;Jing Yang;Caiji Zhang;Xuedi Hao;Shi Meng;Shibin Wang;Zheng Yang;Long Chen;Yanlong Zhao;Shirong Ge

{"title":"基于并行驾驶操作系统的地下矿山自动驾驶：挑战、框架和案例研究","authors":"Bin Tian;Jing Yang;Caiji Zhang;Xuedi Hao;Shi Meng;Shibin Wang;Zheng Yang;Long Chen;Yanlong Zhao;Shirong Ge","doi":"10.1109/TIV.2024.3450609","DOIUrl":null,"url":null,"abstract":"Autonomous driving plays a crucial role in the development of intelligent mines. However, the complex environments in mines present many challenges for the application of autonomous driving compared to urban scenes. Especially in underground mines, the environments such as dust, water mist, narrow roads, and sharp turnings bring additional difficulties for autonomous driving. In response to these issues, a framework of autonomous driving in underground mines based on parallel driving operation systems was proposed. It consists of the intelligent scheduling and management platform, autonomous trackless rubber-tyred vehicle, V2X cooperative perception system, and remote driving system. Field tests were conducted in two real mines to validate the effectiveness of the solution. The experiments demonstrate that our proposed solution boosts the automation level of transportation operations, ensuring operational efficiency and enhancing the safety of transportation processes in underground mines.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"10 5","pages":"3268-3277"},"PeriodicalIF":14.3000,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Autonomous Driving in Underground Mines via Parallel Driving Operation Systems: Challenges, Frameworks and Cases Study\",\"authors\":\"Bin Tian;Jing Yang;Caiji Zhang;Xuedi Hao;Shi Meng;Shibin Wang;Zheng Yang;Long Chen;Yanlong Zhao;Shirong Ge\",\"doi\":\"10.1109/TIV.2024.3450609\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Autonomous driving plays a crucial role in the development of intelligent mines. However, the complex environments in mines present many challenges for the application of autonomous driving compared to urban scenes. Especially in underground mines, the environments such as dust, water mist, narrow roads, and sharp turnings bring additional difficulties for autonomous driving. In response to these issues, a framework of autonomous driving in underground mines based on parallel driving operation systems was proposed. It consists of the intelligent scheduling and management platform, autonomous trackless rubber-tyred vehicle, V2X cooperative perception system, and remote driving system. Field tests were conducted in two real mines to validate the effectiveness of the solution. The experiments demonstrate that our proposed solution boosts the automation level of transportation operations, ensuring operational efficiency and enhancing the safety of transportation processes in underground mines.\",\"PeriodicalId\":36532,\"journal\":{\"name\":\"IEEE Transactions on Intelligent Vehicles\",\"volume\":\"10 5\",\"pages\":\"3268-3277\"},\"PeriodicalIF\":14.3000,\"publicationDate\":\"2024-08-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Intelligent Vehicles\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10654355/\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Intelligent Vehicles","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10654355/","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}

引用次数: 0

摘要

自动驾驶在智能矿山的发展中起着至关重要的作用。然而，与城市场景相比，矿山复杂的环境给自动驾驶的应用带来了许多挑战。特别是在地下矿山，灰尘、水雾、狭窄的道路、急转弯等环境给自动驾驶带来了额外的困难。针对这些问题，提出了一种基于并行驱动操作系统的地下矿山自动驾驶框架。它由智能调度管理平台、自动无轨橡胶轮胎车、V2X协同感知系统和远程驾驶系统组成。在两个实际矿山进行了现场试验，验证了该方案的有效性。实验表明，该方案提高了地下矿山运输作业的自动化水平，保证了作业效率，提高了运输过程的安全性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Autonomous Driving in Underground Mines via Parallel Driving Operation Systems: Challenges, Frameworks and Cases Study

Autonomous driving plays a crucial role in the development of intelligent mines. However, the complex environments in mines present many challenges for the application of autonomous driving compared to urban scenes. Especially in underground mines, the environments such as dust, water mist, narrow roads, and sharp turnings bring additional difficulties for autonomous driving. In response to these issues, a framework of autonomous driving in underground mines based on parallel driving operation systems was proposed. It consists of the intelligent scheduling and management platform, autonomous trackless rubber-tyred vehicle, V2X cooperative perception system, and remote driving system. Field tests were conducted in two real mines to validate the effectiveness of the solution. The experiments demonstrate that our proposed solution boosts the automation level of transportation operations, ensuring operational efficiency and enhancing the safety of transportation processes in underground mines.

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

IEEE Transactions on Intelligent Vehicles Mathematics-Control and Optimization

CiteScore

12.10

自引率

13.40%

发文量

177

期刊介绍： The IEEE Transactions on Intelligent Vehicles (T-IV) is a premier platform for publishing peer-reviewed articles that present innovative research concepts, application results, significant theoretical findings, and application case studies in the field of intelligent vehicles. With a particular emphasis on automated vehicles within roadway environments, T-IV aims to raise awareness of pressing research and application challenges. Our focus is on providing critical information to the intelligent vehicle community, serving as a dissemination vehicle for IEEE ITS Society members and others interested in learning about the state-of-the-art developments and progress in research and applications related to intelligent vehicles. Join us in advancing knowledge and innovation in this dynamic field.