A novel throttle-based self-stabilizing control scheme integrated into an anisotropic continuum model to mitigate cyber-attacks in connected vehicle scenarios

Abstract

Since the unveiling of the technical architecture for vehicle-road-cloud unity, the technological advancements and industrial development of intelligent connected vehicles (ICVs) have been accelerated. However, the open inter-vehicle communication environment exposes ICVs to potential security threats from malicious hackers, which can compromise information sharing by tampering with transmission data. To address these challenges, we introduce the intensity coefficient of cyber-attacks into an original microscopic traffic flow model. In our model, a compensation algorithm leveraging historical data to fill the missing traffic information caused by malevolent cyber-attacks, and a self-stabilizing control scheme relying on electronic throttle (ET) angle dynamics are designed to enhance the robustness of traffic flow. The corresponding macroscopic version of the microscopic model is derived using a macroscopic-microscopic transformation approach. Through the implementation of the small perturbation approach, the stability criteria are identified by completing a linear stability analysis regarding the new models. The results show that the intensity coefficient of cyber-attacks, time gap and control gain term are intimately linked to the stability of traffic flow. We also determined the KdV-Burger's equation by performing the nonlinear analysis, and present its associated density wave solution. Finally, numerical simulations of the proposed model are conducted by adopting the first-order upwind approach and the principle of finite difference under both open and periodic boundary conditions. The simulation results corroborate the theoretical findings and also validate that the new model can mimic some typical scenarios in real-world scenarios, such as the accumulation and dissipation process of road traffic flow.