The integration of emotion-aware systems into automotive safety frameworks is an evolving field in the application of artificial intelligence (AI) and human-computer interaction (HCI). This paper proposes a comprehensive, human-centric model designed to monitor drivers' emotional states in real-time, providing adaptive feedback to minimize risks associated with stress, fatigue, anger and distraction. By addressing critical gaps in the literature, such as the integration of multi-sensory data, the provision of real-time adaptive responses, and the emphasis on user-centered design, this model advances the field of intelligent automotive safety systems. The proposed framework combines affective computing with advanced machine learning algorithms to create a system capable of strengthening drivers' safety and well-being.