Driving Towards Safety: Online PPG-based Drowsiness Detection with TCNs

Increasing driver and driving safety is one of the most compelling needs of the automotive industry, both in terms of economic and social impact. Current approaches primarily focus on analyzing unsafe vehicle behavior, often overlooking the critical factor of the driver's physiological state. This paper introduces a novel solution leveraging temporal convolutional networks (TCNs) for unobtrusive driver drowsiness detection based on photoplethysmography (PPG). PPG data is collected seamlessly using sensors integrated into the steering wheel, providing a non-invasive assessment of the autonomic nervous system (ANS). We benchmarked our model on 16 subjects using a leave-one-subject-out (LOSO) cross-validation scheme, achieving an average accuracy of 77.03%. The model also shows good performance in avoiding false alarms when the driver is alert with a false positive ratio of just 8.21% and correctly detecting drowsiness with a low false negative ratio of 13.92%, improving the state-of-the-art for PPG based approaches. A quantized version of the model is deployed on a commercial ultra-low-power (ULP) system-on-a-chip (SoC), demonstrating real-world feasibility with an inference time of 4.8 ms and energy per inference of 117 μJ. This work represents a significant step towards unobtrusive, real-time physiological monitoring in driving environments, contributing to the ongoing efforts to improve driver's safety.