Self-stabilization control on traffic flow of connected and automated vehicles under cyberattacks

As traffic systems are becoming increasingly interconnected and automated, it is crucial to protect important systems from cyberattacks nowadays. In this study, we propose the Self-Stabilizing Cyberattack (SS-CA) model to investigate the connection between self-stabilizing control and the impact of cyberattacks on traffic flow dynamics in the context of connected vehicles. The linear stability analysis examines the stability criteria for the SS-CA model. Nonlinear analysis uses reductive perturbation methods to derive soliton solutions, providing descriptions of traffic density wave propagation. From the findings, it is evident that, as a cyberattack's intensity increases, traffic stability decreases while increasing the self-stabilization control parameter enhances traffic stability. Furthermore, the effect of self-stabilizing control over headway is found effective in avoiding the negative impact of cyberattacks, which decreases traffic flow stability. The study validates theoretical insights through numerical simulations demonstrating the significance of self-stabilizing behavior in mitigating traffic disruptions caused by cyberattacks.