\textit{Intelligent Navigation Systems} (INS) are exposed to an increasing number of informational attack vectors, which often intercept through the communication channels between the INS and the transportation network during the data collecting process. To measure the resilience of INS, we use the concept of a Wardrop Non-Equilibrium Solution (WANES), which is characterized by the probabilistic outcome of learning within a bounded number of interactions. By using concentration arguments, we have discovered that any bounded feedback delaying attack only degrades the systematic performance up to order $\tilde{\mathcal{O}}(\sqrt{{d^3}{T^{-1}}})$ along the traffic flow trajectory within the Delayed Mirror Descent (DMD) online-learning framework. This degradation in performance can occur with only mild assumptions imposed. Our result implies that learning-based INS infrastructures can achieve Wardrop Non-equilibrium even when experiencing a certain period of disruption in the information structure. These findings provide valuable insights for designing defense mechanisms against possible jamming attacks across different layers of the transportation ecosystem.

翻译：\textit{智能导航系统}（INS）面临日益增多的信息攻击向量，这些攻击通常通过数据采集过程中INS与交通网络之间的通信信道进行拦截。为衡量INS的韧性，我们采用沃德罗普非均衡解（WANES）概念，该概念以有限交互次数内学习的概率结果表征。通过集中性论证，我们发现，在延迟镜像下降（DMD）在线学习框架内，任何有界反馈延迟攻击仅会沿交通流轨迹将系统性能退化至$\tilde{\mathcal{O}}(\sqrt{{d^3}{T^{-1}}})$阶，且该性能退化仅需较弱假设即可实现。研究结果表明，即使信息结构经历一定时期的破坏，基于学习的INS基础设施仍能实现沃德罗普非均衡。这些发现为设计跨交通生态系统各层面对抗潜在干扰攻击的防御机制提供了重要启示。