Feedback control of a heterogeneous lattice hydrodynamic model with multi-visual field effect under cyber-attacks

As intelligent driving technology and vehicle communication systems advance rapidly, the future road networks are expected to be dominated by a heterogeneous traffic mix of connected autonomous vehicles (CAVs) and human-driven vehicles (HDVs). However, malicious cyber-attacks targeting CAVs can create discrepancies between the traffic data they receive and actual conditions, thereby increasing traffic risks. This study employs a lattice hydrodynamics framework that incorporates multi-visual field effects, proposing a model with a feedback control strategy to counteract the adverse effects of cyber-attacks. Linear stability analysis yields the neutral stability curve, while nonlinear analysis leads to a modified Korteweg-de Vries (mKdV) equation describing the evolution of traffic congestion near the critical point. Theoretical results are validated through numerical simulations under periodic boundary conditions. Additionally, power spectrum and spectral entropy analysis provide deeper insights into how the proportion of CAVs, cyber-attacks, and feedback control influence traffic stability. The simulations indicate that a higher CAV ratio can enhance system stability. Conversely, false traffic information received by the vehicle control unit can trigger traffic collapse or diminish traffic efficiency. The proposed feedback control strategy effectively alleviates these negative impacts from cyber-attacks, enhancing both the stability and efficiency of mixed traffic systems. These findings are further substantiated by power spectrum and spectral entropy analyses of density evolution.