How are pedestrian safety compromised under suppressed warning cyberattacks at a connected intersection? – Exploring vehicle-pedestrian interactions using a hidden markov model-based approach

Pedestrians are among the most vulnerable road users at intersections, and their safety risks are expected to decrease while connected vehicle (CV) warning systems can support driver awareness and yielding behavior. However, cyberattacks that suppress pedestrian warnings may fundamentally alter driver-pedestrian interactions in ways that are difficult to observe in real traffic. This study examines how cyberattacks targeting pedestrian warnings affect pedestrian safety. A controlled driving-simulator experiment was conducted with 32 human drivers to collect real-world vehicle-pedestrian interaction trajectories under both benchmark and cyberattack conditions. In the cyberattack scenario, pedestrian warnings were removed from the dashboard display. To address the limited scale of experimental data and the high variability of pedestrian motion, this study proposes a Hidden Markov Model (HMM)-based generative framework integrating surrogate safety measurements (SSM) to augment the vehicle-pedestrian interaction. Driving simulation analysis shows that cyberattacks can bring significant hazards to vehicle-pedestrian interaction, with a larger reduction in time to collision. At the warning phase, the stop dynamic types' safety is degraded by cyberattacks, with speed increasing over time. The HMM-generated trajectories show that increasing pedestrian speed consistently reduces safety in both benchmark and cyberattack scenarios, with safety measurement being particularly sensitive at low pedestrian speeds (< 0.8 m/s). Under these low-speed conditions, cyberattacks have a more pronounced adverse impact on physically farther pedestrians, especially when the pedestrian-warning distance is greater than 15 m. The results offer insights for the design of pedestrian warning systems and cyber-resilient traffic safety strategies.