{"title":"采矿中的嵌入式智能:利用多模态大语言模型实现矿山自动驾驶","authors":"Luxi Li;Yuchen Li;Xiaotong Zhang;Yuhang He;Jianjian Yang;Bin Tian;Yunfeng Ai;Lingxi Li;Andreas Nüchter;Zhe Xuanyuan","doi":"10.1109/TIV.2024.3417938","DOIUrl":null,"url":null,"abstract":"With advancements in computer technology, the benefits of embodied intelligence are increasingly evident. This interactive learning model allows AI to be more flexibly deployed across diverse fields. Recent developments in multi-modal large language models (LLMs) have accelerated AI progress, especially in autonomous driving. This perspective highlights how embodied intelligence can enhance LLM applications in the mining industry, presenting new opportunities and potential to revolutionize the field. It also examines the challenges of deploying embodied agents in mining and offers insights into future research and development.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 5","pages":"4831-4834"},"PeriodicalIF":14.0000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Embodied Intelligence in Mining: Leveraging Multi-Modal Large Language Models for Autonomous Driving in Mines\",\"authors\":\"Luxi Li;Yuchen Li;Xiaotong Zhang;Yuhang He;Jianjian Yang;Bin Tian;Yunfeng Ai;Lingxi Li;Andreas Nüchter;Zhe Xuanyuan\",\"doi\":\"10.1109/TIV.2024.3417938\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With advancements in computer technology, the benefits of embodied intelligence are increasingly evident. This interactive learning model allows AI to be more flexibly deployed across diverse fields. Recent developments in multi-modal large language models (LLMs) have accelerated AI progress, especially in autonomous driving. This perspective highlights how embodied intelligence can enhance LLM applications in the mining industry, presenting new opportunities and potential to revolutionize the field. It also examines the challenges of deploying embodied agents in mining and offers insights into future research and development.\",\"PeriodicalId\":36532,\"journal\":{\"name\":\"IEEE Transactions on Intelligent Vehicles\",\"volume\":\"9 5\",\"pages\":\"4831-4834\"},\"PeriodicalIF\":14.0000,\"publicationDate\":\"2024-03-01\",\"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/10569079/\",\"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/10569079/","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}
Embodied Intelligence in Mining: Leveraging Multi-Modal Large Language Models for Autonomous Driving in Mines
With advancements in computer technology, the benefits of embodied intelligence are increasingly evident. This interactive learning model allows AI to be more flexibly deployed across diverse fields. Recent developments in multi-modal large language models (LLMs) have accelerated AI progress, especially in autonomous driving. This perspective highlights how embodied intelligence can enhance LLM applications in the mining industry, presenting new opportunities and potential to revolutionize the field. It also examines the challenges of deploying embodied agents in mining and offers insights into future research and development.
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.