2025年4月4日 周五
首页本刊简介投稿须知编委会成员论文撰写模板论文格式要求过刊全文链接审稿单联系我们English
首页 > 过刊浏览>2022年第50卷第12期 >1715-1727. DOI:10.11908/j.issn.0253-374x.22406
PDF HTML阅读 XML下载 导出引用 引用提醒
一种网络攻击下网联自动车的改进换道模型
CSTR:
[cstr]
作者:
作者单位:

1.北京航空航天大学交通科学与工程学院 北京 100191;2.Department of Civil and Environmental Engineering，Rensselaer Polytechnic Institute， Troy， NY 12180， United States

作者简介:

吴新开（1979—），博士，教授.研究方向为智能交通系统、自动驾驶、电动汽车及汽车信息安全。 E-mail： xinkaiwu@buaa.edu.cn

中图分类号:

U461.6

基金项目:

国家自然科学基金青年基金（52002013）；国家自然科学基金面上项目（61773040）


An Improved Lane-changing Model for Connected Automated Vehicles Under Cyberattacks
Author:
Affiliation:

1.School of Transportation Science and Engineer Beihang University， Beijing 100191， China;2.Department of Civil and Environmental Engineering， Rensselaer Polytechnic Institute， Troy， NY 12180， United States

  • 摘要
    • | |
  • 访问统计
    • |
  • 参考文献 [26]
    • |
  • 相似文献 [20]
    • | | |
  • 文章评论
    摘要:

    网联自动车被认为可以提升交通效率、保证行车安全并节约能源，但是由于无线通信系统的开放性，网联自动车很容易受到网络攻击的威胁。现有的研究主要集中于网络攻击的种类及过程，并评估该攻击对车辆纵向行为的影响。本文旨在研究网络攻击对车辆横向行为的影响，即对网络攻击下的换道行为进行研究。通过对经典的跟驰模型智能驾驶员模型（Intelligent Driver Model， IDM）和经典的换道模型最小化换道总制动模型（Minimizing Overall Braking Induced by Lane changes， MOBIL）进行改进，提出了一种扩展换道模型（Extended Lane-Changing model， ELC），来对网络攻击影响下的车辆换道行为进行建模分析。最后通过仿真实验，说明了不同的恶意网络攻击对车辆换道行为的影响。结果表明，网络攻击会显著影响车辆的换道决策，并导致异常驾驶行为。

    Abstract:

    The connected automated vehicle （CAV） is promising to enhance traffic efficiency， traveling safety， and energy savings. However， due to the open wireless communication， the CAV is vulnerable to cyber threats. Existing studies mainly focus on surveying related cyberattacks and evaluating the impact of attacks on vehicular longitudinal behaviors on a single lane. This paper aims to investigate the effects of cyberattacks on vehicular lateral behaviors on a two-lane highway， i.e.， the lane-changing （LC） behaviors under cyberattacks. Based on a classical lane-changing model--minimizing overall braking induced by lane changes （MOBIL） model， and a classical car-following model--Intelligent Driver Model （IDM）， this study proposes an extended lane-changing model （ELC） which can model CAV's lane-changing behaviors under cyberattacks. At the end， simulations are conducted to illustrate the impact of different malicious attacks on vehicles' LC movements. Results show that cyberattacks can imperil the LC maneuvers and lead to abnormal driving behaviors.

    参考文献
    [1] TIAN D, WU G, BORIBOONSOMSIN K, et al. Performance measurement evaluation framework and co-benefit/tradeoff analysis for connected and automated vehicles (cav) applications: a survey[J]. IEEE Intelligent Transportation Systems Magazine, 2018,10(3): 110.
    [2] SARKER A, SHEN H, RAHMAN M, et al. A review of sensing and communication, human factors, and controller aspects for information-aware connected and automated vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2019,21(1): 7.
    [3] PAPADOULIS A, QUDDUS M, IMPRIALOU M. Evaluating the safety impact of connected and autonomous vehicles on motorways[J]. Accident Analysis & Prevention, 2019,124: 12.
    [4] FERRAG M A, MAGLARAS L A, JANICKE H, et al. Authentication protocols for internet of things: a comprehensive survey[J]. Security and Communication Networks, 2017,2017: 1.
    [5] ALNASSER A, SUN H, JIANG J. Cyber security challenges and solutions for v2x communications: a survey[J]. Computer Networks, 2019,151: 52.
    [6] KHATTAK Z H, SMITH B L, FONTAINE M D. Impact of cyberattacks on safety and stability of connected and automated vehicle platoons under lane changes[J]. Accident Analysis & Prevention, 2021,150: 105861.
    [7] HADDAD J, MIRKIN B. Resilient perimeter control of macroscopic fundamental diagram networks under cyberattacks[J]. Transportation research part B: methodological, 2020,132: 44.
    [8] LOPEZ A, JIN W, FARUQUE M A AL . Security analysis for fixed-time traffic control systems[J]. Transportation Research Part B: Methodological, 2020,139: 473.
    [9] FENG Y, HUANG S, CHEN Q A, et al. Vulnerability of traffic control system under cyberattacks with falsified data[J]. Transportation research record, 2018, 2672(1): 1.
    [10] KHALID KHAN S, SHIWAKOTI N, STASINOPOULOS P. A conceptual system dynamics model for cybersecurity assessment of connected and autonomous vehicles[J]. Accident Analysis & Prevention, 2022,165: 106515.
    [11] MAGLARAS L, FERRAG M, DERHAB A, et al. Threats, countermeasures and attribution of cyber attacks on critical infrastructures[J]. EAI Endorsed Transactions on Security and Safety, 2018,5(16): 1.
    [12] DONG C, WANG H, NI D, et al. Impact evaluation of cyber-attacks on traffic flow of connected and automated vehicles[J]. IEEE Access, 2020(8): 86824.
    [13] LI Y, TU Y, FAN Q, et al. Influence of cyber-attacks on longitudinal safety of connected and automated vehicles[J]. Accident Analysis & Prevention, 2018,121: 148.
    [14] HU J, QI L, ZHANG Z, et al. A detection method for cyber-attack on connected signal phase and timing information[J]. Transportmetrica B: Transport Dynamics, 2022,10(1): 731.
    [15] KHATTAK Z H, SMITH B L, FONTAINE M D. Impact of cyberattacks on safety and stability of connected and automated vehicle platoons under lane changes[J]. Accident Analysis & Prevention, 2021,150: 105861.
    [16] KASHYAP A, CHAKRAVARTHY A, MENON P P. Detection of cyber-attacks in automotive traffic using macroscopic models and gaussian processes[J]. IEEE Control Systems Letters, 2022,6: 1688.
    [17] ZHAO X, ABDO A, LIAO X, et al. Evaluating Cybersecurity Risks of Cooperative Ramp Merging in Mixed Traffic Environments[J]. IEEE Intelligent Transportation Systems Magazine, 2022,14(6):52.
    [18] KESTING A, TREIBER M, HELBING D. General lane-changing model mobil for car-following models[J]. Transportation Research Record, 2007,1999(1): 86.
    [19] TREIBER M, HENNECKE A, HELBING D. Congested traffic states in empirical observations and microscopic simulations[J]. Physical Review E, 2000,62(2): 1805.
    [20] SALVUCCI D D, LIU A. The time course of a lane change: driver control and eye-movement behavior[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2002,5(2): 123.
    [21] KESTING A, TREIBER M, SCH?NHOF M, et al. Adaptive cruise control design for active congestion avoidance[J]. Transportation Research Part C: Emerging Technologies, 2008,16(6): 668.
    [22] ZHENG Y, RAN B, QU X, et al. Cooperative lane changing strategies to improve traffic operation and safety nearby freeway off-ramps in a connected and automated vehicles environment[J]. IEEE Transactions on Intelligent Transportation Systems, 2019,21(11): 4605.
    [23] ZHENG Y, ZHANG G, LI Y, et al. Optimal jam-absorption driving strategy for mitigating rear-end collision risks with oscillations on freeway straight segments[J]. Accident Analysis & Prevention, 2020,135: 105367.
    [24] ALIWA E, RANA O, PERERA C, et al. Cyberattacks and countermeasures for in-vehicle networks[J]. ACM Computing Surveys, 2021,54(1): 1.
    [25] WANG P, WU X, HE X. Modeling and analyzing cyberattack effects on connected automated vehicular platoons[J]. Transportation Research Part C: Emerging Technologies, 2020,115: 102625.
    [26] ZHANG T, YE D. False data injection attacks with complete stealthiness in cyber–physical systems: a self-generated approach[J]. Automatica, 2020,120: 109117.
    引证文献
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

吴新开,何山,张少伟,贺晓征,王斯奋.一种网络攻击下网联自动车的改进换道模型[J].同济大学学报(自然科学版),2022,50(12):1715~1727

复制
分享
文章指标
  • 点击次数:1225
  • 下载次数: 437
  • HTML阅读次数: 90
  • 引用次数: 0
历史
  • 收稿日期:2022-10-20
  • 在线发布日期: 2023-01-03
文章二维码

您是本站第 10435797 访问者

版权所有：《同济大学学报（自然科学版）》     主管单位：教育部    主办单位：同济大学

地址：上海市四平路1239号同济大学内    邮编：200092    电话：021-65982344     E-mail：zrxb@tongji.edu.cn

本系统由北京勤云科技发展有限公司设计