Deep Learning-based rPPG Models Towards Automotive Applications: A Benchmark Study

Paper in proceeding, 2025

Remote photoplethysmography (rPPG) has the potential to significantly enhance driver safety systems by enabling the detection of critical conditions, such as driver drowsiness and sudden illness, through non-invasive monitoring of cardio-respiratory functions. However, the dynamic environment within a vehicle, characterized by motion artifacts and varying illumination, presents unique challenges for accurate rPPG estimation. In this study, we conducted a comprehensive benchmark of various supervised and unsupervised rPPG algorithms using the MR-NIRP car dataset to assess their performance in automotive settings. Qualitative and quantitative experiments were performed to evaluate and compare several rPPG models designed in stable, noise-controlled environments, highlighting the impact of real-world conditions on model performance. Our findings highlight the promise of machine learning approaches, particularly neural network-based models, in overcoming these challenges and accurately estimating heart and res-piration rates in real-world driving scenarios. This study underscores the potential for integrating rPPG-based mon-itoring systems into vehicles to enhance driver safety and well-being.