A Contactless and Non-Intrusive System for Driver's Stress Detection

Abstract

Stress plays a significant role in fatal accidents, highlighting the importance of timely monitoring of driver stress to facilitate effective interventions and reduce road accidents. However, monitoring driver stress presents numerous challenges in the context of driving. First, state-of-the-art techniques such as self-stress evaluation and periodic cortisol level checks are not suitable for the driving scenario. Second, existing unimodal solutions does not provide a comprehensive and holistic assessment of the driver's stress. Although some research utilizes multimodal features, the use of wearables attached to the driver's body in real-life situations is impractical and highly discomforting. Our proposed solution tackles these challenges by offering a contactless and non-intrusive approach that prioritizes the driver's comfort during the collection of multimodal data, which includes capturing heart rate variability (HRV), respiration rate, and microfacial expressions. Through feature-level data fusion, we combine and integrate these diverse modalities to generate comprehensive insights. These insights are then utilized by the multimodal learning pipeline to predict the driver's stress levels in real driving scenarios.