Collaborative Train and Edge Computing in Edge Intelligence Based Train Autonomous Operation Control Systems

火车 准时 强化学习 计算机科学 GSM演进的增强数据速率 边缘计算 计算 智能交通系统 计算卸载 人工智能 分布式计算 实时计算 工程类 土木工程 地图学 算法 运输工程 地理
作者
Li Zhu,Taiyuan Gong,Siyu Wei,F. Richard Yu
出处
期刊:IEEE Transactions on Intelligent Transportation Systems [Institute of Electrical and Electronics Engineers]
卷期号:25 (9): 11991-12004 被引量:2
标识
DOI:10.1109/tits.2024.3382747
摘要

Train autonomous circumambulate systems (TACS) are a new-generation train control systems. They are characterized by autonomous travel path planning, autonomous protection, and autonomous train operation adjustment. One crucial problem in TACS is real-time communication and computation of autonomous train control systems. Trains need to obtain the real-time state of all the other trains and derive real-time intelligent control commands in TACS. With high capacity and reliable 5G technologies, edge intelligence (EI) can perform complex computing tasks offloaded from trains with little delay. In this paper, we develop a collaborative train and edge computing framework for TACS to provide real-time communication and computation service for train control. To reduce the tracking deviations and ensure the train operation punctuality, ride comfort, and energy-saving ability, we adopt the model predictive control (MPC) algorithm to optimize the autonomous train control process. To cope with the limited onboard computing power, we propose a meta reinforcement learning (MRL) based collaborative computing method to solve the computation offloading problem. Compared with the existing RL-based offloading policy that requires sufficient data samples for training, MRL can rapidly adapt to different computation offloading environments, which is exceptionally suited for the urban rail transit system where different rail lines have different operating environments, and we do not have enough data to finish a regular reinforcement learning and training task. Experimental results illustrate that the proposed framework can provide TACS with reliable and real-time computing services. The train operational efficiency can be significantly improved with our proposed collaborative computing train control algorithm.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
shylockcai发布了新的文献求助10
1秒前
lyl完成签到,获得积分10
1秒前
Jocd完成签到,获得积分10
1秒前
Akim应助孙二二采纳,获得10
2秒前
优雅的千凝完成签到,获得积分10
2秒前
Time完成签到,获得积分10
3秒前
格拉希尔完成签到 ,获得积分10
4秒前
Yzh完成签到,获得积分10
5秒前
shuke完成签到,获得积分10
6秒前
啊七飞完成签到,获得积分10
6秒前
琦琦国王完成签到,获得积分10
7秒前
务实的绝悟完成签到,获得积分10
10秒前
经纲完成签到 ,获得积分0
10秒前
我说苏卡你说不列完成签到,获得积分10
12秒前
CipherSage应助科研通管家采纳,获得10
14秒前
14秒前
顾矜应助科研通管家采纳,获得10
14秒前
14秒前
劲秉应助科研通管家采纳,获得10
14秒前
sun应助科研通管家采纳,获得20
14秒前
苗条绝义应助科研通管家采纳,获得10
15秒前
隐形曼青应助科研通管家采纳,获得10
15秒前
科研通AI5应助科研通管家采纳,获得10
15秒前
蔡从安完成签到,获得积分20
15秒前
劲秉应助科研通管家采纳,获得10
15秒前
CipherSage应助科研通管家采纳,获得10
15秒前
NexusExplorer应助科研通管家采纳,获得10
15秒前
黑囡应助科研通管家采纳,获得10
15秒前
15秒前
科研通AI5应助科研通管家采纳,获得10
15秒前
科研通AI2S应助科研通管家采纳,获得10
15秒前
5165asd完成签到 ,获得积分10
16秒前
ritata完成签到 ,获得积分10
17秒前
小彤完成签到 ,获得积分10
18秒前
18秒前
ll应助蔡从安采纳,获得10
19秒前
boltos完成签到,获得积分20
20秒前
善学以致用应助橙猫猫采纳,获得10
21秒前
GEZIKU完成签到 ,获得积分10
23秒前
24秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Mechanistic Modeling of Gas-Liquid Two-Phase Flow in Pipes 2500
Structural Load Modelling and Combination for Performance and Safety Evaluation 1000
Conference Record, IAS Annual Meeting 1977 720
電気学会論文誌D(産業応用部門誌), 141 巻, 11 号 510
Typology of Conditional Constructions 500
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3566811
求助须知:如何正确求助?哪些是违规求助? 3139560
关于积分的说明 9431989
捐赠科研通 2840353
什么是DOI,文献DOI怎么找? 1560990
邀请新用户注册赠送积分活动 730141
科研通“疑难数据库(出版商)”最低求助积分说明 717855