Multimodal interior comfort evaluation via odor and vision information

This study addresses the limited monitoring perspectives and lack of comprehensive comfort evaluation models in ride-hailing services by introducing a genetic algorithm-optimized visual-odor multimodal in-car comfort assessment system. Specifically, the system leverages on-board cameras to capture images of passenger seating arrangements, upon which a VGG-19-based cleanliness evaluation subnetwork is constructed to effectively extract and identify cleanliness attributes within the vehicle cabin. Focusing on the common odors encountered in vehicles, an in-car odor detection apparatus is designed using MQ series odor sensors and an STM32 microcontroller. Furthermore, an odor pseudo-image encoder and an air quality evaluation subnetwork, grounded on odor concentration monitoring values, are proposed to enable the extraction and recognition of vehicle interior odor characteristics. Integrating the cleanliness and odor features, this work proposes a genetic algorithm-optimized visual-odor multimodal comfort evaluation network model, facilitating a quantitative assessment of multi-dimensional in-car comfort. Moreover, an intuitive mobile app interface is developed to display real-time data and evaluation results, thereby enhancing the ride-hailing experience. Experimental results obtained in a simulated in-car setting demonstrate that, in comparison to existing methodologies, the proposed visual-odor multimodal evaluation method for in-car comfort offers superior accuracy in assessing the in-car environment.