Wide-Angle Thermal Sensing for Personalized Climate Control: An Infrared Fisheye Camera Approach in Commuter Vehicles

Abstract: Thermal comfort is a critical aspect of electric vehicle (EV) design, particularly in shared mobility scenarios where passengers with diverse comfort preferences frequently enter and exit. Current climate control systems are primarily reliant on traditional sensors such as those for temperature, sun load, air quality, etc. However, these systems are often unable to provide personalized thermal comfort in dynamic environments, especially in autonomous vehicles where passenger numbers, seating arrangements, and environmental conditions can change significantly. In this study, a novel solution is proposed, utilizing a single infrared (IR) fisheye camera to monitor and optimize thermal comfort across the entire vehicle cabin. The camera is used to provide a full 360-degree view of the vehicle interior, allowing simultaneous monitoring of postures, body temperature, heat dissipation, and environmental factors such as solar heat gain or cold air drafts. This approach enables the dynamic tracking of multiple passengers, ensuring that changes in occupancy and cabin conditions are accommodated in real time. While the focus of the study is placed on an autonomous electric commuter vehicle (AECV) developed at the Karlsruhe Institute of Technology and its partners, the approach is applicable to a range of other vehicles and transportation systems.To process the thermal data, machine learning and deep learning techniques are employed. Spatial features are extracted from the thermal images using convolutional neural networks, allowing patterns such as body temperature distribution and localized hot or cold zones to be identified. Additionally, temporal changes in passenger movement and cabin conditions are modeled using recurrent neural networks, or more specifically, long short-term memory networks, enabling the prediction of thermal preferences based on historical and real-time data. Training of these models is conducted on datasets that combine thermal imagery with contextual behavioral data, such as posture and gestures, detected by a standard fisheye camera in previous studies.The predictions generated by the system are designed to guide real-time HVAC adjustments to provide personalized comfort. For example, cooling is applied to areas where passengers are exposed to sunlight, while targeted heating is activated for those in cooler zones. Validation of the system is planned through controlled laboratory experiments and real-world trials in shared mobility scenarios. These evaluations will assess the ability of the system to monitor thermal comfort accurately, respond dynamically to changes in occupancy, and optimize energy usage when compared to traditional HVAC systems.This single-camera approach is designed to streamline design, reduce costs, and enable advanced thermal comfort systems in dynamic environments. While its primary focus is on optimizing thermal conditions in electric vehicles, the system also presents opportunities to address motion sickness, a common issue in autonomous vehicle passengers. Additionally, its potential applications extend beyond transportation, offering promising avenues in areas such as indoor heating and cooling.

Keywords: Thermal comfort, Passenger monitoring, Human-centric design, Shared mobility, Machine learning, Deep learning, Infrared fisheye camera

DOI: 10.54941/ahfe1005871

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