网络层的信息流与车辆层的物理主体之间的高级集成和交互使联网的自动车辆(CAV)能够实现快速,合作和共享旅行。然而,网络层受到恶意攻击和通信资源短缺的挑战,这使得车辆层受到系统非线性的影响,扰动随机性和行为不确定性,从而干扰了排的稳定运行。到目前为止,学者通常采用假设或改进跟车模型的方法来探索网络攻击中的排行行为和防御机制,但是他们没有考虑模型本身是否对网络攻击防御有干扰和影响。换句话说,目前仍在确定所设计的跟随车模型是否可以完全适用于此类网络攻击。为车辆层建模提供理论依据,有必要理解不同的跟车车型在各种网络攻击中的自我抵抗力。首先,我们回顾了网络攻击中车辆层采用的跟随车模型,涉及交通工程,物理统计,和排动态。根据审查,我们将网络层面临的恶意攻击分为显性攻击和隐性攻击。第二,我们建立了一个合作广义力模型(CGFM),它组合和统一了遵循通信拓扑的r-preaders。提出的模型,标记为脆弱的协作智能驱动程序模型(VCIDM),脆弱合作最优速度模型(VCOVM),和脆弱合作排动力学模型(VCPDM),结合CGFM模型和各种网络攻击注入模式来解释网络攻击对排自我抵抗能力的影响。根据描述的模型,我们从基本交通要素的三个维度提供六个指标,包括司机,车辆,和环境。这些指标说明了驾驶员的容忍度,车辆适应性,当一个排面临诸如虚假信息之类的攻击时,重播/延迟,通信中断。我们对跟车模型和网络攻击注入模式进行了整理和重组,完成了对车队自我抵抗能力的研究,这对于增强车载层的内生安全和提高网络层的入侵容忍度具有积极的研究价值和现实意义。
The high-level integration and interaction between the information flow at the cyber layer and the physical subjects at the vehicular layer enables the connected automated vehicles (CAVs) to achieve rapid, cooperative and shared travel. However, the cyber layer is challenged by malicious attacks and the shortage of communication resources, which makes the vehicular layer suffer from system nonlinearity, disturbance randomness and behavior uncertainty, thus interfering with the stable operation of the platoon. So far, scholars usually adopt the method of assuming or improving the car-following model to explore the platoon behavior and the defense mechanism in
cyberattacks, but they have not considered whether the model itself has disturbance and impact on cyberattack defenses. In other words, it is still being determined whether the car-following model designed can be fully applicable to such
cyberattacks. To provide a theoretical basis for vehicular layer modeling, it is necessary to comprehend the self-resistance of different car-following models faced on various
cyberattacks. First, we review the car-following models adopted on the vehicular layer in
cyberattacks, involving traffic engineering, physical statistics, and platoon dynamics. Based on the review, we divide the malicious attacks faced by the cyber layer into explicit attacks and implicit attacks. Second, we develop a cooperative generalized force model (CGFM), which combines and unifies the r-predecessors following communication topology. The proposed models, labeled the vulnerable cooperative intelligent driver model (VCIDM), the vulnerable cooperative optimal velocity model (VCOVM), and the vulnerable cooperative platoon dynamics model (VCPDM), incorporate the CGFM model and assorted cyberattack injection modes to explain the cyberattack effects on the platoon self-resistance capability. Upon the described models, we provide six indicators in three dimensions from the basic traffic element, including drivers, vehicles, and environment. These indicators illustrate driver tolerance, vehicle adaptability, and environmental resistance when a platoon faces attacks such as bogus information, replay/delay, and communication interruption. We arrange and reorganize the car-following models and the cyberattack injection modes to complete the research on the self-resistance capability of the platoon, which has positive research value and practical significance for enhancing the endogenous security at the vehicular layer and improving the intrusion tolerability at the cyber layer.