Overview of metro train driving technology development:from manual driving to intelligent unmanned driving

Wenzhu LAI; Dewang CHEN; Zhenfeng HE; Xinguo DENG; CARLO Marano GIUSEPPE

doi:10.11959/j.issn.2096-6652.202204

您当前的位置：

首页 >

文章列表页 >

Overview of metro train driving technology development:from manual driving to intelligent unmanned driving

Surveys and Prospectives | 更新时间：2024-06-05

- Overview of metro train driving technology development:from manual driving to intelligent unmanned driving
- Chinese Journal of Intelligent Science and Technology Vol. 4, Issue 3, Pages: 335-343(2022)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61976055);The Special Fund for Education and Scientific Research of Fujian Provincial Department of Finance(GY-Z21001)
- DOI：10.11959/j.issn.2096-6652.202204
  CLC： TP273，TP314.41
- Published Online：2022-09，
  
  Published：15 September 2022
- 稿件说明：
移动端阅览
Wenzhu LAI, Dewang CHEN, Zhenfeng HE, et al. Overview of metro train driving technology development:from manual driving to intelligent unmanned driving[J]. Chinese journal of intelligent science and technology, 2022, 4(3): 335-343.
DOI：

Wenzhu LAI, Dewang CHEN, Zhenfeng HE, et al. Overview of metro train driving technology development:from manual driving to intelligent unmanned driving[J]. Chinese journal of intelligent science and technology, 2022, 4(3): 335-343. DOI： 10.11959/j.issn.2096-6652.202204.

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。

Abstract

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 driving

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

Keywords

references

《隧道建设(中英文)》编辑部 . 世界地铁数据·2018 [J ] . 隧道建设(中英文) , 2018 , 38 ( 12 ): 2077 - 2080 .

Editorial Department of Tunnel Construction . World metro data,2018 [J ] . Tunnel Construction , 2018 , 38 ( 12 ): 2077 - 2080 .

韩宝明 , 陈佳豪 , 杨运节 , 等 . 2019 年世界城市轨道交通运营统计与分析综述 [J ] . 都市快轨交通 , 2020 , 33 ( 1 ): 4 - 8 .

HAN B M , CHEN J H , YANG Y J , et al . Statistical analysis of urban rail transit operation in the world in 2019:a review [J ] . Urban Rapid Rail Transit , 2020 , 33 ( 1 ): 4 - 8 .

孙世超 , 顾保南 , 邓澄远 . 2015年中国城市轨道交通运营线路统计和分析:中国城市轨道交通“年报快递”之三 [J ] . 城市轨道交通研究 , 2016 , 19 ( 1 ): 1 - 4 , 9 .

SUN S C , GU B N , DENG C Y . Statistical analysis of urban rail transit lines in 2015 China—express delivery of annual report on urban rail transit Ⅲ [J ] . Urban Mass Transit , 2016 , 19 ( 1 ): 1 - 4 , 9 .

曹炳坤 . 世界地铁发展令人瞩目 [J ] . 城市公共交通 , 2003 ( 5 ): 331 .

CAO B K . The world’s remarkable development of subway [J ] . Urban Public Transport , 2003 ( 5 ): 331 .

CAO Y , WANG Z C , LIU F , et al . Bio-inspired speed curve optimization and sliding mode tracking control for subway trains [J ] . IEEE Transactions on Vehicular Technology , 2019 , 68 ( 7 ): 6331 - 6342 .

HUANG J , LIU Y , XIA Y N , et al . Train driving data learning with S-CNN model for gear prediction and optimal driving [C ] // Proceedings of 2019 Chinese Automation Congress . Piscataway:IEEE Press , 2019 : 2227 - 2232 .

PU Q , ZHU X M , ZHANG R T , et al . Speed profile tracking by an adaptive controller for subway train based on neural network and PID algorithm [J ] . IEEE Transactions on Vehicular Technology , 2020 , 69 ( 10 ): 10656 - 10667 .

YIN J T , SU S , XUN J , et al . Data-driven approaches for modeling train control models:comparison and case studies [J ] . ISA Transactions , 2020 , 98 : 349 - 363 .

ZHANG C Y , CHEN D W , YIN J T , et al . Data-driven train operation models based on data mining and driving experience for the diesel-electric locomotive [J ] . Advanced Engineering Informatics , 2016 , 30 ( 3 ): 553 - 563 .

宿帅 , 朱擎阳 , 魏庆来 , 等 . 基于DQN的列车节能驾驶控制方法 [J ] . 智能科学与技术学报 , 2020 , 2 ( 4 ): 372 - 384 .

SU S , ZHU Q Y , WEI Q L , et al . A DQN-based approach for energy-efficient train driving control [J ] . Chinese Journal of Intelligent Science and Technology , 2020 , 2 ( 4 ): 372 - 384 .

MIDYA S , THOTTAPPILLIL R . An overview of electromagnetic compatibility challenges in European Rail Traffic Management System [J ] . Transportation Research Part C:Emerging Technologies , 2008 , 16 ( 5 ): 515 - 534 .

汤力成 . 考虑ATO控制策略的城轨列车推荐速度曲线优化 [D ] . 北京:北京交通大学 , 2018 .

TANG L C . Optimization of recommended speed curve considering ATO control strategy for urban rail transit [D ] . Beijing:Beijing Jiaotong University , 2018 .

DONG H R , NING B , CAI B G , et al . Automatic train control system development and simulation for high-speed railways [J ] . IEEE Circuits and Systems Magazine , 2010 , 10 ( 2 ): 6 - 18 .

KWON S D , LEE H S . A study of unmanned light rail vehicle (K-AGT) system engineering process under safety requirement,IEC 62267 [J ] . Journal of the Korea Society of Systems Engineering , 2013 , 9 ( 1 ): 1 - 11 .

赵红 . 列车自动驾驶系统(ATO 系统)在地铁中的应用 [J ] . 电子世界 , 2014 ( 7 ): 30 - 30 , 32 .

ZHAO H . Application of automatic train driving system (ATO) in subway system [J ] . Electronics World , 2014 ( 7 ): 30 - 30 , 32 .

冯立勋 . 浅谈上海地铁 17 号线车辆特点 [J ] . 中国高新区 , 2018 ( 18 ): 46 .

FENG L X . On the characteristics of Shanghai metro line 17 [J ] . Science ＆ Technology Industry Parks , 2018 ( 18 ): 46 .

饶东 . 全自动运行系统地铁车辆技术 [J ] . 铁路技术创新 , 2015 ( 4 ): 13 - 17 .

RAO D . Metro vehicle technology with automatic operation system [J ] . Railway Technical Innovation , 2015 ( 4 ): 13 - 17 .

缪根欣 . 巴黎地铁 MP89 型新动车组的试验 [J ] . 控制与信息技术 , 1995 ( 7 ): 20 .

MIAO G X . Test of MP89 EMU of Paris metro [J ] . Control and Information Technology , 1995 ( 7 ): 20 .

任安萍 . 浅谈我国全自动无人驾驶地铁的发展 [J ] . 科技视界 , 2012 ( 25 ): 207 - 208 .

REN A P . The development of fully automated and driverless subway in China [J ] . Science ＆ Technology Vision , 2012 ( 25 ): 207 - 208 .

傅世善 . 闭塞与列控概论第十讲移动闭塞概述 [J ] . 铁路通信信号工程技术 , 2006 , 3 ( 4 ): 61 - 63 .

FU S S . Introduction to block and train control lecture 10 overview of moving block [J ] . Railway Signalling＆ Communication Engineering , 2006 , 3 ( 4 ): 61 - 63 .

赵有明 , 方鸣 , 刘潍清 . 欧洲轨道交通技术类研究项目综述 [J ] . 现代城市轨道交通 , 2014 ( 1 ): 1 - 6 .

ZHAO Y M , FANG M , LIU W Q . Overview on technology research projects in rail transit in European [J ] . Modern Urban Transit , 2014 ( 1 ): 1 - 6 .

张崔中 . 我国首条全自动驾驶地铁“上岗” [J ] . 新长征(党建版) , 2017 ( 5 ): 60 .

ZHANG C Z . China’s first fully automatic Metro “on duty” [J ] . New Long March , 2017 ( 5 ): 60 .

宗明 , 郜春海 , 何燕 . 基于 CBTC 控制的全自动驾驶系统 [J ] . 都市快轨交通 , 2006 , 19 ( 3 ): 34 - 36 .

ZONG M , GAO C H , HE Y . Fully automatic operation system based on CBTC [J ] . Urban Rapid Rail Transit , 2006 , 19 ( 3 ): 34 - 36 .

王浩 , 林湛 , 杨霓霏 , 等 . 轨道交通国产化 CBTC 系统技术创新点研究 [J ] . 铁路计算机应用 , 2014 , 23 ( 1 ): 58 - 60 .

WANG H , LIN Z , YANG N F , et al . Research on technical innovative points of domestic CBTC system of urban transit [J ] . Railway Computer Application , 2014 , 23 ( 1 ): 58 - 60 .

冷勇林 . 基于专家经验和机器学习的列车智能驾驶算法研究 [D ] . 北京:北京交通大学 , 2013 .

LENG Y L . Intelligent train operation algorithm based on expert knowledge and machine learning [D ] . Beijing:Beijing Jiaotong University , 2013 .

陈德旺 , 章明亮 , 沈镛 . 智慧地铁:基于大数据与人工智能的新型地铁系统 [J ] . 智能城市 , 2018 , 4 ( 19 ): 8 - 10 .

CHEN D W , ZHANG M L , SHEN Y . Intelligent metro:new metro system based on big data and artificial intelligence [J ] . Intelligent City , 2018 , 4 ( 19 ): 8 - 10 .

WANG H N , LIU Z , ZHANG Y Y , et al . Overview of deep reinforcement learning [J ] . Frontiers of Information Technology ＆ Electronic Engineering , 2020 , 21 ( 12 ): 63 - 82 .

LIU R , GOLOVITCHER I M . Energy-efficient operation of rail vehicles [J ] . Transportation Research Part A:Policy and Practice , 2003 , 37 ( 10 ): 917 - 932 .

REBENTROST P , MOHSENI M , LLOYD S . Quantum support vector machine for big data classification [J ] . Physical Review Letters , 2014 , 113 ( 13 ): 130503 .

宁滨 . 平行轨道交通系统 [J ] . 智能科学与技术学报 , 2019 , 1 ( 3 ): 215 - 218 .

NING B . Parallel rail transportation system [J ] . Chinese Journal of Intelligent Science and Technology , 2019 , 1 ( 3 ): 215 - 218 .

FIORE U , PALMIERI F , CASTIGLIONE A , et al . Network anomaly detection with the restricted Boltzmann machine [J ] . Neurocomputing , 2013 , 122 : 13 - 23 .

HUANG W H , SONG G J , HONG H K , et al . Deep architecture for traffic flow prediction:deep belief networks with multitask learning [J ] . IEEE Transactions on Intelligent Transportation Systems , 2014 , 15 ( 5 ): 2191 - 2201 .

KARPATHY A , TODERICI G , SHETTY S , et al . Large-scale video classification with convolutional neural networks [C ] // Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2014 : 1725 - 1732 .

SUTTON R S , BARTO A G . Reinforcement learning [J ] . A Bradford Book , 1998 , 15 ( 7 ): 665 - 685 .

CORLU C G , DE LA TORRE R , SERRANO-HERNANDEZ A , , et al . Optimizing energy consumption in transportation:literature review,insights,and research opportunities [J ] . Energies , 2020 , 13 ( 5 ): 1115 .

鲁明旭 , 叶银忠 , 马向华 . 神经网络在地铁客流预测中的应用 [J ] . 机械研究与应用 , 2012 , 25 ( 3 ): 86 - 89 .

LU M X , YE Y Z , MA X H . Application of neural network in the subway passenger flow prediction [J ] . Mechanical Research ＆ Application , 2012 , 25 ( 3 ): 86 - 89 .

AI B , MOLISCH A F , RUPP M , et al . 5G key technologies for smart railways [J ] . Proceedings of the IEEE , 2020 , 108 ( 6 ): 856 - 893 .

施浚珲 . 人工智能在无人驾驶轨道列车中的应用 [J ] . 科技风 , 2019 ( 31 ): 8 - 9 .

SHI J H . Application of artificial intelligence in unmanned rail train [J ] . Science and technology wind , 2019 ( 31 ): 8 - 9 .

吴飞 , 廖彬兵 , 韩亚洪 . 深度学习的可解释性 [J ] . 航空兵器 , 2019 , 26 ( 1 ): 39 - 46 .

WU F , LIAO B B , HAN Y H . Interpretability for deep learning [J ] . Aero Weaponry , 2019 , 26 ( 1 ): 39 - 46 .

郑庆华 . 人机混合增强智能的典型应用专题前言 [J ] . 计算机研究与发展 , 2020 , 57 ( 12 ): 2479 - 2480 .

ZHENG Q H . Foreword of typical application of human machine hybrid enhanced intelligence [J ] . Computer Research and Development , 2020 , 57 ( 12 ): 2479 - 2480 .

郑南宁 . 人工智能新时代 [J ] . 智能科学与技术学报 , 2019 , 1 ( 1 ): 1 - 3 .

ZHENG N N . The new era of artificial intelligence [J ] . Chinese Journal of Intelligent Science and Technology , 2019 , 1 ( 1 ): 1 - 3 .

YIN J T , CHEN D W , LI L X . Intelligent train operation algorithms for subway by expert system and reinforcement learning [J ] . IEEE Transactions on Intelligent Transportation Systems , 2014 , 15 ( 6 ): 2561 - 2571 .

ZHOU K C , SONG S J , XUE A K , et al . Smart train operation algorithms based on expert knowledge and reinforcement learning [J ] . IEEE Transactions on Systems,Man,and Cybernetics:Systems , 2022 , 52 ( 2 ): 716 - 727 .

陈德旺 , 蔡际杰 , 黄允浒 . 面向可解释性人工智能与大数据的模糊系统发展展望 [J ] . 智能科学与技术学报 , 2019 , 1 ( 4 ): 327 - 334 .

CHEN D W , CAI J J , HUANG Y H . Development prospect of fuzzy system oriented to interpretable artificial intelligence and big data [J ] . Chinese Journal of Intelligent Science and Technology , 2019 , 1 ( 4 ): 327 - 334 .

赵文迪 , 陈德旺 , 卓永强 , 等 . 深度神经模糊系统算法及其回归应用 [J ] . 自动化学报 , 2020 , 46 ( 11 ): 2350 - 2358 .

ZHAO W D , CHEN D W , ZHUO Y Q , et al . Deep neural fuzzy system algorithm and its regression application [J ] . Acta Automatica Sinica , 2020 , 46 ( 11 ): 2350 - 2358 .

Views

1149

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Research on metro train driverless system based on man-machine hybrid intelligence

Digital chefs and intelligent cooking systems based on multimodal large language model

New AI for new frontier of intelligent science and technology: from PAPAI++ to new enterprise ecology

K21 education and intelligent sciences: future schools and teachers/students in future society

Digital teachers and parallel education: A paradigm shift in teaching and learning after ChatGPT

Related Author

Benzun HUANG

Dewang CHEN

Zhenfeng HE

Xinguo DENG

Marano GIUSEPPECARLO

SUN Yueshuo

LI Xinyuan

LI Bai

Related Institution

College of Mechanical and Vehicle Engineering, Hunan University

State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle, Hunan University

State Key Laboratory for Multi-modal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences

State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences

School of Artificial Intelligence, University of Chinese Academy of Sciences

Postal code：100079
Tel：（010）53879206 Email：cjist@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09064830号-19 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
Total Visits：35685 Visits Today：160

⁰