Road safety remains a persistent global challenge, with overtaking maneuvers presenting particularly complex and dangerous scenarios. These maneuvers necessitate rapid calculations by drivers, blending assessments of speed, distance, and the potential actions of other vehicles. Misjudgments, whether stemming from inattention, inaccurate perception, or insufficient reaction time, can have devastating consequences. While existing research in traffic safety has yielded valuable tools, there's a crucial need for methods that more accurately and flexibly capture the risks inherent in overtaking. The prevalent use of Time to Collision (TTC) in risk assessment, while valuable, has limitations when potential collisions aren't imminent. To address this, we propose a novel approach: a probabilistic collision model designed specifically for overtaking scenarios. This model incorporates both longitudinal and lateral distances between vehicles to provide a dynamic and nuanced evaluation of risk. This has significant practical implications. Vehicle systems, empowered by this model, can proactively alert drivers when overtaking maneuvers become increasingly hazardous, even without immediate TTC warnings. This paper also explores how innovation can drive industrial practices that align with the UN's Sustainable Development Goals.