A DEEP LEARNING FRAMEWORK FOR MULTI-SENSOR THERMAL COMFORT PREDICTION IN PASSENGER CARS
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Using a combination of environmental and physiological sensors to gather real-time data on the cabin's circumstances and the occupants' responses, a useful deep learning framework is created to predict the comfort and stress levels of passengers in a passenger car cabin. This system analyses the intricate relationships between a number of variables, including temperature, humidity, airflow, and occupant biometrics, using advanced deep learning algorithms. This makes it possible for it to estimate thermal comfort levels and possible heat stress in a precise and dynamic manner. Passengers' health, safety, and comfort during travel can be improved by integrating the system into passenger cars to enable proactive climate control modifications. It tackles issues brought on by shifting environmental circumstances and individual differences in heat sensitivity. This is an example of how AI-driven sensor fusion might improve human-centered climate control in automobiles.
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Gaur, S. (2026). A Deep Learning Framework for Multi-Sensor Thermal Comfort Prediction in Passenger Cars. International Journal of Science, Strategic Management and Technology, 02(6). https://doi.org/10.55041/ijsmt.v2i6.096
Gaur, Sunny. "A Deep Learning Framework for Multi-Sensor Thermal Comfort Prediction in Passenger Cars." International Journal of Science, Strategic Management and Technology, vol. 02, no. 6, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i6.096.
Gaur, Sunny. "A Deep Learning Framework for Multi-Sensor Thermal Comfort Prediction in Passenger Cars." International Journal of Science, Strategic Management and Technology 02, no. 6 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i6.096.
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