{"url":"/dataset/autotherm","name":"AutoTherm","full_name":null,"description_markdown":"# Temporal Dataset for Indoor and In-Vehicle Thermal Comfort Estimation\r\n\r\n## Abstract\r\nThermal comfort estimation is essential for enhancing user experience in static indoor environments and dynamic in-vehicle scenarios. While traditional datasets focus on buildings, their application to fast-changing conditions, such as in vehicles, remains unexplored. We address this gap by introducing two temporal datasets collected from (1) a self-built climatic chamber with 31 sensor signals and user-labeled ratings from 18 participants and (2) in-vehicle studies with 20 participants in a BMW 3 Series. \r\n\r\nOur results show that leveraging time-series data significantly improves thermal comfort prediction, with recurrent neural network models outperforming single-vector baselines. We benchmark our datasets against publicly available ones, demonstrating superior predictive performance and insights into key signal importance. \r\n\r\n## Key Contributions\r\n- **Datasets**: Temporal multimodal datasets for indoor and in-vehicle thermal comfort estimation.\r\n- **Machine Learning Models**: Comparative studies using recurrent architectures for state recognition and prediction.\r\n- **Signal Importance**: Identification of key factors like ambient temperature, relative humidity, and skin response.\r\n- **Benchmarking**: Evaluation against existing thermal comfort datasets.\r\n\r\n## Keywords\r\nThermal comfort, temporal datasets, machine learning, recurrent neural networks, automotive research","description_withheld":null,"homepage":"","introduced_date":"2024-09-09","introduced_date_note":null,"introduced_by":{"paper":null,"title":"AutoTherm: A Dataset and Benchmark for Thermal Comfort Estimation Indoors and in Vehicles","first_author":null,"url":null},"license":{"name":"MIT","url":"https://choosealicense.com/licenses/mit/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Audio","url":"/datasets/modality/audio"},{"name":"Time series","url":"/datasets/modality/time-series"},{"name":"Tracking","url":"/datasets/modality/tracking"},{"name":"EEG","url":"/datasets/modality/eeg"}],"tasks":[],"languages":[],"variants":["AutoTherm"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}