Dual-phase prediction model of passenger thermal sensation using facial thermal imaging and environmental factors

The concept of passenger-centric design has become increasingly significant in the intelligent automotive industry. Enhancing in-cabin thermal comfort while maintaining energy efficiency is crucial, and to this end, we leverage real-time passenger thermal sensation as a basis for HVAC control strategies. This study introduces a non-invasive, dual-phase prediction model that evaluates passenger thermal comfort by utilizing facial thermal imaging and environmental factors. The proposed model employs a deep learning neural network to extract pertinent features from the facial thermal images for an initial prediction. Then this prediction is refined by integrating it with environmental data through a machine learning algorithm, facilitating real-time personal thermal comfort assessment. Data collection for the model was performed across two distinct seasons, with 22 participants in summer and 18 in winter, gathering facial thermal images, environmental parameters, and thermal sensation votes to construct a comprehensive dataset. Analysis revealed a set of critical environmental factors that correlate strongly with thermal sensation votes, which were used as auxiliary features in the model. The performance of the proposed dual-phase approach, combining deep learning and machine learning techniques, demonstrated robust effectiveness on both summer and winter datasets. This substantiates the method's practicality for real-world application within vehicle environments.