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作 者:赖文柱 陈德旺 何振峰[1,2] 邓新国[1,2] GIUSEPPE CARLO Marano LAI Wenzhu;CHEN Dewang;HE Zhenfeng;DENG Xinguo;GIUSEPPE CARLO Marano(College of Computer and Data Science,Fuzhou University,Fuzhou 350108;China 2.Key Laboratory of Intelligent Metro of Universities in Fujian,Fuzhou University,Fuzhou 350108;China 3.School of Transportation,Fujian University of Technology,Fuzhou 350118;China 4.Politecnico di Torino,Turin EI10129,Italy)
机构地区:[1]福州大学计算机与大数据学院,福建福州350108 [2]福州大学智慧地铁福建省高校重点实验室,福建福州350108 [3]福建工程学院交通运输学院,福建福州350118 [4]都灵理工大学,意大利都灵EI10129
出 处:《智能科学与技术学报》2022年第3期335-343,共9页Chinese Journal of Intelligent Science and Technology
基 金:国家自然科学基金资助项目(No.61976055,No.61906043);福建省财政厅教育科研专项资金(No.GY-Z21001);福州大学人才项目(No.510809/GXRC-19037,No.510730/XRC-18075)。
摘 要:基于国内外地铁列车驾驶技术的发展现状,提出并阐述了地铁列车驾驶技术发展的4个阶段为人工驾驶、自动驾驶、无人驾驶、智能无人驾驶。概括了我国无人驾驶地铁列车的建设情况,针对目前基于神经网络这类机器学习方法的列车控制方法可解释性差的弊端,引入了深度模糊系统的概念,提出了基于人机混合智能的地铁智能无人驾驶基本框图,为将处理紧急情况的专家经验、人工智能算法和无人驾驶系统结合起来,实现智能无人驾驶提供了一种具有前景的解决思路。Based on the current development status of subway train driving technology in China and abroad,the four stages of subway train driving technology development were proposed and explained as manual driving,automatic driv-ing,unmanned driving,and intelligent unmanned driving.After summarizing the construction situation of unmanned subway trains in China,the disadvantages of the current train control methods based on neural network-based machine learning methods were addressed.Then,the basic block diagram of metro intelligent unmanned driving based on man-machine hybrid intelligence was put forward and the deep fuzzy system was introduced.A promising solution for the combination of expert experience in dealing with emergency and interpretable AI algorithms for unmanned driving system to evolve into intelligent unmanned driving was provided.
关 键 词:地铁 无人驾驶 人工智能 智能无人驾驶 人机混合智能
分 类 号:TP273[自动化与计算机技术—检测技术与自动化装置] TP314.41[自动化与计算机技术—控制科学与工程]
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