The interplay of passing, driver attention, and cyber attack-induced information delays on traffic stability

With the growing integration of Advanced Driver Assistance Systems (ADAS) in modern vehicles, cybersecurity threats present substantial risks to traffic stability and safety. This study introduces an advanced car-following model that accounts for the effects of passing, driver attention, and information delays caused by cyberattacks, and investigates their impact on traffic dynamics within ADAS systems. To understand how perturbations evolve over time, stability analysis is performed. Linear stability analysis identifies the conditions for neutral stability, while nonlinear analysis, using the reductive perturbation method, reveals stable, metastable, and unstable traffic states. The results show that a lower passing rate slightly reduces congestion while maintaining the same flow pattern, whereas a higher passing rate leads to a transition from congested to chaotic flow. Our findings also suggest that cyber intrusions exacerbate instability, while information from leading vehicles helps to enhance stability. Random, variable cyberattack intensity on vehicles is found to be more detrimental to stability than a constant, uniform impact, making traffic flow more unpredictable. Furthermore, spectral entropy is used to quantify traffic disruptions caused by cyberattacks, emphasizing the importance of driver attention and information exchange in mitigating instability. This study provides valuable insights into the interaction between cybersecurity and traffic stability, contributing to the development of more resilient ADAS-based transportation systems.