边缘智能在轨道交通中的应用：前景与展望

朱力; 龚泰源; 梁豪; 唐涛; 王悉; 王洪伟

doi:10.11999/JEIT220116

边缘智能在轨道交通中的应用：前景与展望

doi: 10.11999/JEIT220116

北京交通大学电子信息工程学院北京 100044

基金项目: 国家自然科学基金(61973026)

详细信息

作者简介:
朱力：男，教授，研究方向为交通智能控制与优化

龚泰源：男，博士生，研究方向为交通智能控制与优化

梁豪：男，博士生，研究方向为交通智能控制与优化

唐涛：男，教授，研究方向为轨道交通运行控制系统国产化

王悉：男，副教授，研究方向为交通智能控制与优化

王洪伟：男，副教授，研究方向为交通智能控制与优化

通讯作者:
朱力　lizhu@bjtu.edu.cm

中图分类号: TP18; TP39
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- 被引次数: 0
出版历程
- 收稿日期: 2022-01-27
- 修回日期: 2022-06-20
- 网络出版日期: 2202-06-29
- 刊出日期: 2023-04-10

Application of Edge Intelligence in Rail Transit: Prospects and Future Outlook

School of Electronic Information Engineering, Beijing Jiaotong University, Beijing 100044, China

Funds: The National Natural Science Foundation of China (61973026)

摘要

摘要: 边缘智能作为一项新兴技术，正受到国内外学者的广泛关注，其作为人工智能技术与边缘计算技术的结合，有望促进人工智能技术在各行业的部署，加速产业智能化进程。该文首先介绍了边缘智能技术的基本原理、系统架构及其比较优势，梳理了边缘智能技术的国内外研究现状；分析了边缘智能在轨道交通建设工程、运维调度、智能控制、改造升级的全生命周期应用前景，详述了边缘智能技术在轨道交通过程管理控制、建设现场数据采集分析、信息共享、智能运维、智能调度、自动驾驶系统、列车协同控制及改造升级等全生命周期中的赋能作用。该文随后设计与实现了轨道交通智能运行控制为背景下的边缘智能平台，测试基于深度学习和强化学习的边缘智能应用的功能及性能。最后，归纳了边缘智能技术在轨道交通领域应用的问题与挑战。该文的研究期望为轨道交通领域的边缘智能应用提供有益的借鉴和实践基础。
- 边缘智能 /
- 轨道交通运输 /
- 人工智能 /
- 边缘计算
Abstract: As an emerging technology, edge intelligence is receiving extensive attention from scholars at home and abroad. As a combination of artificial intelligence technology and edge computing technology, it is expected to promote the deployment of artificial intelligence technology in various industries and accelerate the process of industrial intelligence. In this paper the basic principles, system architecture, and comparative advantages of edge intelligence technology, and sorts out the research status of edge intelligence technology at home and abroad are first introduced. The application prospects of the life cycle, the application of edge intelligence technology in the whole life cycle of rail transit process management and control, construction site data collection and analysis, information sharing, intelligent operation and maintenance, intelligent scheduling, automatic driving system, train coordination control, and transformation and upgrading are described in detail. Then the designs and implements an edge intelligent platform under the background of rail transit intelligent operation control, and tests the functions and performance of edge intelligence applications based on deep learning and reinforcement learning are discussed. Finally, the problems and challenges in the application of edge intelligence technology to the field of rail transit are summarized. The research in this paper is expected to provide a useful reference and practical basis for edge intelligence applications to the field of rail transit.
- Edge intelligence /
- Rail transportation /
- Artificial intelligence /
- Edge computing

HTML全文

图 1 边缘智能基础架构

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图 2 基于边缘智能的轨道交通信息共享

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图 3 基于边缘智能的轨道交通智能运维架构

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图 4 基于边缘智能的轨道交通智能调度架构

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图 5 基于边缘智能的列车协同控制架构

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图 6 基于边缘智能的轨道交通智慧化框架

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图 7 面向轨道交通沿线障碍物识别与测距的边缘智能系统

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图 8 边缘智能列车障碍物告警系统平台

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图 9 云、边、端3种计算模式时延测试

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图 10 卸载策略时延测试

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参考文献(35)

[1]	SATYANARAYANAN M, BAHL P, CACERES R, et al. The case for VM-based cloudlets in mobile computing[J]. IEEE Pervasive Computing, 2009, 8(4): 14–23. doi: 10.1109/MPRV.2009.82
[2]	DENG Shuiguang, ZHAO Hailiang, FANG Weijia, et al. Edge intelligence: The confluence of edge computing and artificial intelligence[J]. IEEE Internet of Things Journal, 2020, 7(8): 7457–7469. doi: 10.1109/JIOT.2020.2984887
[3]	ZHOU Zhi, CHEN Xu, LI En, et al. Edge intelligence: Paving the last mile of artificial intelligence with edge computing[J]. Proceedings of the IEEE, 2019, 107(8): 1738–1762. doi: 10.1109/JPROC.2019.2918951
[4]	PELTONEN E, BENNIS M, CAPOBIANCO M, et al. 6G white paper on edge intelligence[J]. arXiv preprint arXiv: 2004.14850, 2020.
[5]	朱卫国. 城市轨道交通综述[J]. 城市车辆, 2001(3): 37–40. ZHU Weiguo. An overview of urban rail transportation[J]. Urban Vehicles, 2001(3): 37–40.
[6]	张殿业, 金键, 杨京帅. 城市轨道交通安全研究体系[J]. 都市快轨交通, 2004, 17(4): 1–3. doi: 10.3969/j.issn.1672-6073.2004.04.001 ZHANG Dianye, JIN Jian, and YANG Jingshuai. The safety research system of urban rail transit[J]. Urban Rapid Rail Transit, 2004, 17(4): 1–3. doi: 10.3969/j.issn.1672-6073.2004.04.001
[7]	史天运. 中国高速铁路信息化现状及智能化发展[J]. 科技导报, 2019, 37(6): 53–59. SHI Tianyun. Present situation of wide applications of information and intelligence in the field of high-speed railway in China[J]. Science &Technology Review, 2019, 37(6): 53–59.
[8]	王同军. 智能铁路总体架构与发展展望[J]. 铁路计算机应用, 2018, 27(7): 1–8. doi: 10.3969/j.issn.1005-8451.2018.07.003 WANG Tongjun. Overall framework and development prospect of intelligent railway[J]. Railway Computer Application, 2018, 27(7): 1–8. doi: 10.3969/j.issn.1005-8451.2018.07.003
[9]	刘芽, 刘占英, 麻永华, 等. 基于云计算技术的城市轨道交通信息化平台发展探索[J]. 现代城市轨道交通, 2019(9): 121–125. LIU Ya, LIU Zhanying, MA Yonghua, et al. Exploration on development of urban rail transit informatization platform based on cloud computing technology[J]. Modern Urban Transit, 2019(9): 121–125.
[10]	张春杰, 武智博, 张硕桐. 物联网及人工智能技术在城市轨道交通综合监控系统中的应用探究[J]. 电子世界, 2020(7): 55–56. doi: 10.19353/j.cnki.dzsj.2020.07.029 ZHANG Chunjie, WU Zhibo, and ZHANG Shuotong. Exploring the application of Internet of things and artificial intelligence technology in urban rail transit integrated monitoring system[J]. Electronics World, 2020(7): 55–56. doi: 10.19353/j.cnki.dzsj.2020.07.029
[11]	魏秀琨, 所达, 魏德华, 等. 机器视觉在轨道交通系统状态检测中的应用综述[J]. 控制与决策, 2021, 36(2): 257–282. doi: 10.13195/j.kzyjc.2020.1199 WEI Xiukun, SUO Da, WEI Dehua, et al. A survey of the application of machine vision in rail transit system inspection[J]. Control and Decision, 2021, 36(2): 257–282. doi: 10.13195/j.kzyjc.2020.1199
[12]	周超, 林湛, 李樊, 等. 城市轨道交通视频监控系统云边协同技术应用研究[J]. 铁道运输与经济, 2020, 42(12): 106–110,125. doi: 10.16668/j.cnki.issn.1003-1421.2020.12.18 ZHOU Chao, LIN Zhan, LI Fan, et al. Cloud-edge collaboration technology of CCTV system for urban rail transit[J]. Railway Transport and Economy, 2020, 42(12): 106–110,125. doi: 10.16668/j.cnki.issn.1003-1421.2020.12.18
[13]	施巍松, 孙辉, 曹杰, 等. 边缘计算: 万物互联时代新型计算模型[J]. 计算机研究与发展, 2017, 54(5): 907–924. doi: 10.7544/issn1000-1239.2017.20160941 SHI Weisong, SUN Hui, CAO Jie, et al. Edge computing-an emerging computing model for the internet of everything era[J]. Journal of Computer Research and Development, 2017, 54(5): 907–924. doi: 10.7544/issn1000-1239.2017.20160941
[14]	O'LEARY D E. Artificial intelligence and big data[J]. IEEE Intelligent Systems, 2013, 28(2): 96–99. doi: 10.1109/MIS.2013.39
[15]	ZHU LI, YU F R, WANG Yige, et al. Big data analytics in intelligent transportation systems: A survey[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(1): 383–398. doi: 10.1109/TITS.2018.2815678
[16]	MICHALSKI R S, CARBONELL J G, and MITCHELL T M. Machine Learning: An Artificial Intelligence Approach[M]. Berlin: Springer Science & Business Media, 2013.
[17]	MENG Yan and LIU Xiyu. Application of K-means algorithm based on ant clustering algorithm in macroscopic planning of highway transportation hub[C]. 2007 First IEEE International Symposium on Information Technologies and Applications in Education, Kunming, China, 2007: 483–488.
[18]	NATH R P D, LEE H J, CHOWDHURY N K, et al. Modified K-means clustering for travel time prediction based on historical traffic data[C]. 14th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, Cardiff, UK, 2010: 511–521.
[19]	HAYDARI A and YILMAZ Y. Deep reinforcement learning for intelligent transportation systems: A survey[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(1): 11–32. doi: 10.1109/TITS.2020.3008612
[20]	ABDULJABBAR R, DIA H, LIYANAGE S, et al. Applications of artificial intelligence in transport: An overview[J]. Sustainability, 2019, 11(1): 189. doi: 10.3390/su11010189
[21]	LECUN Y and RANZATO M. Deep learning tutorial[C]. Tutorials in International Conference on Machine Learning (ICML’13), Atlanta, USA, 2013: 1–29.
[22]	ATZORI L, IERA A, and MORABITO G. The internet of things: A survey[J]. Computer Networks, 2010, 54(15): 2787–2805. doi: 10.1016/j.comnet.2010.05.010
[23]	OUGHTON E J, LEHR W, KATSAROS K, et al. Revisiting wireless internet connectivity: 5G vs Wi-Fi 6[J]. Telecommunications Policy, 2021, 45(5): 102127. doi: 10.1016/j.telpol.2021.102127
[24]	CHEN Xu, JIAO Lei, LI Wenzhong, et al. Efficient multi-user computation offloading for mobile-edge cloud computing[J]. IEEE/ACM Transactions on Networking, 2016, 24(5): 2795–2808. doi: 10.1109/TNET.2015.2487344
[25]	KAI Caihong, ZHOU Hao, YI Yibo, et al. Collaborative cloud-edge-end task offloading in mobile-edge computing networks with limited communication capability[J]. IEEE Transactions on Cognitive Communications and Networking, 2021, 7(2): 624–634. doi: 10.1109/TCCN.2020.3018159
[26]	LI En, ZHOU Zhi, and CHEN Xu. Edge intelligence: On-demand deep learning model co-inference with device-edge synergy[C]. The 2018 Workshop on Mobile Edge Communications, Budapest, Hungary: ACM, 2018: 31–36.
[27]	HAN Song, POOL J, TRAN J, et al. Learning both weights and connections for efficient neural networks[C]. The 28th International Conference on Neural Information Processing Systems, Montreal, Canada, 2015: 1135–1143.
[28]	LI Song, ZHAO Hongli, and MA Jinmin. An edge computing-enabled train obstacle detection method based on YOLOv3[J]. Wireless Communications and Mobile Computing, 2021, 2021: 7670724. doi: 10.1155/2021/7670724
[29]	TANG Jie, LIU Shaoshan, LIU Liangkai, et al. LoPECS: A low-power edge computing system for real-time autonomous driving services[J]. IEEE Access, 2020, 8: 30467–30479. doi: 10.1109/ACCESS.2020.2970728
[30]	DAI Yueyue, XU Du, MAHARJAN S, et al. Joint load balancing and offloading in vehicular edge computing and networks[J]. IEEE Internet of Things Journal, 2019, 6(3): 4377–4387. doi: 10.1109/JIOT.2018.2876298
[31]	GARCIA M H C, MOLINA-GALAN A, BOBAN M, et al. A tutorial on 5G NR V2X communications[J]. IEEE Communications Surveys & Tutorials, 2021, 23(3): 1972–2026. doi: 10.1109/COMST.2021.3057017
[32]	李迎九. 智能建造技术在铁路建设管理中的应用探索[J]. 中国铁路, 2018(5): 1–7. doi: 10.19549/j.issn.1001-683x.2018.05.001 LI Yingjiu. The application of intelligent building technology in railway construction management[J]. China Railway, 2018(5): 1–7. doi: 10.19549/j.issn.1001-683x.2018.05.001
[33]	李得伟, 张天宇, 周玮腾, 等. 轨道交通大数据运用现状及发展趋势研究[J]. 都市快轨交通, 2016, 29(6): 1–7. doi: 10.3969/j.issn.1672-6073.2016.06.001 LI Dewei, ZHANG Tianyu, ZHOU Weiteng, et al. State-of-the-art and trend analysis of big data application in rail transit[J]. Urban Rapid Rail Transit, 2016, 29(6): 1–7. doi: 10.3969/j.issn.1672-6073.2016.06.001
[34]	端嘉盈, 沈海燕, 李智. 边缘计算在铁路“智能车站”物联网中的应用研究[J]. 物联网技术, 2020, 10(10): 53–56,61. doi: 10.16667/j.issn.2095-1302.2020.10.015 DUAN Jiaying, SHEN Haiyan, and LI Zhi. Application research of edge computing in railway "smart station" internet of things[J]. Internet of Things Technologies, 2020, 10(10): 53–56,61. doi: 10.16667/j.issn.2095-1302.2020.10.015
[35]	王悉. 基于机器学习的重载列车智能驾驶方法研究[D]. [博士论文], 北京交通大学, 2017. WANG Xi. Machine learning based intelligent operation methods for heavy haul train[D]. [Ph. D. dissertation], Beijing Jiaotong University, 2017.