A cloud-supported proactive safety decision-making and control system for rear-end collision avoidance in occluded...

Safety control for automated vehicles under occluded visibility has emerged as a key research topic. Nevertheless, prior work largely focuses on risk-aware control based on onboard sensing, with limited discussion of cloud-supported proactive safety decision-making and control (CPSC) for occluded scenarios. To address this gap, we analyze the key characteristics of the vehicle-road-cloud integrated control system (VRCICS) and develop a generic and scalable CPSC architecture across different SAE Levels of Driving Automation. Using the lead-vehicle cut-out with occluded hazard exposure (LVCO) as a representative scenario, we propose a cloud-based TTC interaction-matrix risk assessment method that leverages predicted spatiotemporal trajectory interactions to dynamically identify potential collision points and risk targets. A risk-transfer-aware proactive safety decision algorithm is then developed to enable timely risk warnings and adaptive safe-speed planning. Simulation and ablation studies verify risk-transfer identification and adaptive safety control in potential multi-vehicle rear-end crash chains, and highlight the role of vehicle-side safety functions in the hierarchical architecture. Field tests show that conventional AEB collides at 50 km/h (headway level 1) and 60 km/h (headway level 1/2), whereas CPSC avoids collisions with early warning and proactive deceleration (mean≤4m/s²). Moreover, a sensitivity analysis of roadside perception refinement indicates that the false-trigger rate decreases by 72.7% and the collision-avoidance success rate increases by 18.5%. Additionally, Joint experiments across multiple OEMs confirm the system's engineering feasibility, scalability, and robust performance across diverse vehicle platforms. This study provides a practical guidance and insights for VRCICS-based assisted driving applications.