A Fine-Grained Predictive Stress Quantification Framework for Drivers with Autism Spectrum Disorder in Inclusive Smart Mobility

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that significantly heightens the challenges and risks associated with driving. Individuals with ASD often experience increased sensory sensitivities, difficulty adapting to dynamic environments, and heightened vulnerability to unexpected events, which can lead to elevated stress levels, sensory overload, and impaired decision-making on the road. Existing research in stress detection primarily proposes reactive approaches, triggering responses only after stress has escalated. Such strategies limit the possibility for timely and effective intervention. Moreover, most existing studies fail to quantify stress intensity, providing only binary assessments. This lack of granularity hinders the development of proactive and personalized support mechanisms. In this paper, we propose a novel real-time approach for continuous stress prediction and quantification tailored to drivers with ASD. Our method leverages physiological signals and contextual data to anticipate stress escalation and enable proactive stress quantification and provide a proactive, fine-grained assessment of stress levels, paving the way for personalized and context-aware in-vehicle support systems that enhance driving safety and comfort for individuals with ASD.