Oak Ridge National Laboratory, 1 Bethel Valley Rd, Oak Ridge, TN 37830, United States.
Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA 22904, United States.
Accid Anal Prev. 2021 Feb;150:105861. doi: 10.1016/j.aap.2020.105861. Epub 2021 Jan 11.
Connected and automated vehicles (CAVs) offer a huge potential to improve the operations and safety of transportation systems. However, the use of smart devices and communications in CAVs introduce new risks. CAVs would leverage vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communication, thus providing additional system access points compared to traditional systems. Automation makes these systems more vulnerable and increases the consequences of cyberattacks. This study utilizes an infrastructure-based communication platform consisting of cooperative adaptive cruise control and lane control advisories developed by the authors to perform cyber risk assessment of CAVs. The study emulates three types of cyberattacks (message falsification, dedicated denial of service, and spoofing attacks) in a representative traffic environment consisting of multiple CAV platoons and lane change events to analyze the safety and stability impacts of the cyberattacks. Simulation experiments using VISSIM reveals that traffic stream and CAV string is unstable under all three types of cyberattacks. The worst case is represented by the message falsification attack. Increases in volatility are observed over a no attack case, with variations increasing by an average of 43%-51% along with an increase of over 3000 crash conflicts. Similarly, lane change crash conflicts are observed to be more severe compared to rear end crash conflicts, showing a higher probability of severe injuries. Further, the case of slight cyberattack on a single CAV also creates significant disruption in the traffic stream. Analysis of variance (ANOVA) reveals the statistical significance of the results. These results pave the way for future design of secure systems from a monitoring perspective.
联网和自动驾驶汽车 (CAV) 为改善交通系统的运营和安全提供了巨大的潜力。然而，CAV 中智能设备和通信的使用引入了新的风险。CAV 将利用车对车 (V2V) 和车对基础设施 (V2I) 通信，从而与传统系统相比提供更多的系统接入点。自动化使这些系统更加脆弱，并增加了网络攻击的后果。本研究利用作者开发的基于基础设施的通信平台，包括协同自适应巡航控制和车道控制建议，对 CAV 进行网络风险评估。研究在由多个 CAV 车阵和车道变换事件组成的代表性交通环境中模拟了三种类型的网络攻击（消息伪造、专用拒绝服务和欺骗攻击），以分析网络攻击对安全和稳定性的影响。使用 VISSIM 进行的仿真实验表明，在所有三种类型的网络攻击下，交通流和 CAV 串都是不稳定的。最坏的情况是消息伪造攻击。与无攻击情况相比，波动性增加，变化平均增加 43%-51%，同时冲突冲突增加超过 3000 次。类似地，与追尾碰撞冲突相比，车道变换碰撞冲突更为严重，显示出更严重伤害的更高概率。此外，单个 CAV 轻微网络攻击的情况也会对交通流造成重大干扰。方差分析（ANOVA）揭示了结果的统计学意义。这些结果为从监控角度设计安全系统铺平了道路。
