Data-Driven Based Cruise Control of Connected and Automated Vehicles Under Cyber-Physical System Framework

被引:20
|
作者
Zhang, Tao [1 ,2 ]
Zou, Yuan [1 ,2 ]
Zhang, Xudong [1 ,2 ]
Guo, Ningyuan [1 ,2 ]
Wang, Wenwei [1 ,2 ]
机构
[1] Beijing Inst Technol, Sch Mech Engn, Beijing 100081, Peoples R China
[2] Beijing Collaborat & Innovat Ctr Elect Vehicle, Beijing 100081, Peoples R China
基金
中国国家自然科学基金;
关键词
Cruise control; Cloud computing; Predictive models; Safety; Data models; Vehicle dynamics; Intelligent transportation systems; connected and automated vehicle; cyber-physical system; merging behavior; MODEL-PREDICTIVE CONTROL; ENERGY MANAGEMENT; ELECTRIC VEHICLES; DESIGN;
D O I
10.1109/TITS.2020.2991223
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Cyber-physical systems (CPS) have become the cutting-edge technology for the next generation of industrial applications, and are rapidly developing and inspiring numerous application areas. This article presents an optimal forward-looking distributed CPS application for the safety-following driving control of connected and automated vehicles (CAV) in the intelligent transportation. The relevant components and required technologies of the CPS concept in intelligent transportation systems are introduced firstly. Under this framework, each CAV is considered as an independent CPS. In the safe driving of vehicles, historical data is used to build vehicle behavior prediction models and dynamic driving system models. At the same time, a new range strategy considering the probability of merging behavior is proposed and applied to the CAV's safe cruise control. The results show that through the application framework of CPS, the proposed range strategy can improve the following safety of the vehicle.
引用
收藏
页码:6307 / 6319
页数:13
相关论文
共 50 条
  • [41] Using Formal Methods to Specify Data-Driven Cyber-Physical Systems
    Conradi Hoffmann, Jose Luis
    Horstmann, Leonardo Passig
    Wagner, Matheus
    Vieira, Felipe
    de Lucena, Mateus Martinez
    Frohlich, Antonio Augusto
    2022 IEEE 31ST INTERNATIONAL SYMPOSIUM ON INDUSTRIAL ELECTRONICS (ISIE), 2022, : 643 - 648
  • [42] Data-driven Stealthy Actuator Attack against Cyber-Physical Systems
    Zhang, Zhixue
    Zhang, Qirui
    Liu, Tao
    Pang, Zhonghua
    Cui, Bing
    Jin, Shuxin
    Liu, Kun
    PROCEEDINGS OF THE 39TH CHINESE CONTROL CONFERENCE, 2020, : 4395 - 4399
  • [43] Towards Data-Driven Reliability Modeling for Cyber-Physical Production Systems
    Friederich, Jonas
    Lazarova-Molnar, Sanja
    12TH INTERNATIONAL CONFERENCE ON AMBIENT SYSTEMS, NETWORKS AND TECHNOLOGIES (ANT) / THE 4TH INTERNATIONAL CONFERENCE ON EMERGING DATA AND INDUSTRY 4.0 (EDI40) / AFFILIATED WORKSHOPS, 2021, 184 : 589 - 596
  • [44] A data-driven cyber-physical approach for personalised smart, connected product co-development in a cloud-based environment
    Zheng, Pai
    Xu, Xun
    Chen, Chun-Hsien
    JOURNAL OF INTELLIGENT MANUFACTURING, 2020, 31 (01) : 3 - 18
  • [45] An integrated data-driven scheme for the defense of typical cyber-physical attacks
    Wu, Shimeng
    Jiang, Yuchen
    Luo, Hao
    Zhang, Jiusi
    Yin, Shen
    Kaynak, Okyay
    RELIABILITY ENGINEERING & SYSTEM SAFETY, 2022, 220
  • [46] A data-driven cyber-physical approach for personalised smart, connected product co-development in a cloud-based environment
    Pai Zheng
    Xun Xu
    Chun-Hsien Chen
    Journal of Intelligent Manufacturing, 2020, 31 : 3 - 18
  • [47] Smart buildings as Cyber-Physical Systems: Data-driven predictive control strategies for energy efficiency
    Schmidt, Mischa
    Ahlund, Christer
    RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2018, 90 : 742 - 756
  • [48] Cross-layered distributed data-driven framework for enhanced smart grid cyber-physical security
    Starke, Allen
    Nagaraj, Keerthiraj
    Ruben, Cody
    Aljohani, Nader
    Zou, Sheng
    Bretas, Arturo
    McNair, Janise
    Zare, Alina
    IET SMART GRID, 2022, 5 (06) : 398 - 416
  • [49] Interval-based/Data-driven Risk Management for Intelligent Vehicles: Application to an Adaptive Cruise Control System
    Ben Lakhal, Nadhir Mansour
    Adouane, Lounis
    Nasri, Othman
    Slama, Jaleleddine Ben Hadj
    2019 30TH IEEE INTELLIGENT VEHICLES SYMPOSIUM (IV19), 2019, : 239 - 244
  • [50] Data-Driven Nonlinear Adaptive Optimal Control of Connected Vehicles
    Gao, Weinan
    Jiang, Zhong-Ping
    NEURAL INFORMATION PROCESSING (ICONIP 2017), PT VI, 2017, 10639 : 122 - 129