A Synergistic, Non-Invasive Sensing-Fusion Approach for Predictive Kinetosis Monitoring in Autonomous Vehicles

Abstract

The coming of autonomous vehicles promises a "passenger economy," a vision jeopardized by the challenge of kinetosis (motion sickness). Effective mitigation requires non-invasive, predictive monitoring, yet current methods are impractical. This paper presents a robust methodology that validates a synergistic fusion of thermal and visible-light imaging as a reliable respiratory biomarker. Our system employs a thermal camera to track temperature differentials at the nostrils and an RGB camera to monitor thoraco-abdominal movements. We introduce a real-time signal processing pipeline featuring: (1) dynamic, multi-modal region-of-interest tracking, (2) independent signal "activity gating" to reject noise, and (3) a temporal peak-fusion algorithm to compute a single, robust breathing rate. The primary contribution is the demonstration of this system's technical feasibility and resilience to real-world failure modes. In a pilot study, we demonstrate high accuracy against a ground-truth metric and, crucially, show the system maintains a stable output during facial and torso occlusions that would cause single-modality systems to fail. This robust, non-invasive system represents an important technical step toward truly human-centric autonomous vehicles such as the C2CBridge Vehicle.

Keywords: Kinetosis, Motion Sickness, Computer Vision, Thermal Imaging, Breath Frequency, Human Centred Vehicle Design, C2CBridge

DOI: 10.54941/ahfe1007123