Collisions involving vulnerable road users (VRUs) represent a major concern in contemporary mobility, especially in urban contexts where attentional demands, occlusions, and complex interactions can challenge drivers’ ability to anticipate risk. Supporting driver situation awareness through adaptive interfaces and intelligent sensing is a promising strategy, particularly when combined with personalized modeling of attentional and behavioral states. This paper presents a human-centered research framework developed to explore the use of distributed sensing and adaptive interaction for enhancing road safety. Starting from a set of high-risk VRU interaction scenarios, such as occluded crossings, unexpected overtaking, and low-visibility intersections, a design rationale is outlined for using real-time data from vehicle sensors, roadside infrastructure, and driver monitoring to generate timely, non-intrusive, and individualized feedback via the HMI. The research focuses on the development of a simulation-based infrastructure for evaluating adaptive safety strategies based on driver-specific patterns of attention and behavior, in alignment with the principles of the Driver Digital Twin (DrDT) paradigm. Methodological insight is provided into the design of this experimental infrastructure, the key performance indicators used, and the experimental hypotheses. This work contributes a theoretically grounded and experimentally structured setup that may inform similar efforts in driving simulations for studying personalized safety interaction strategies.