Omkar2118/datasets huggingface dataset:
NTU4DRadLM

NTU4DRadLM is a novel 4D radar dataset specifically proposed for research on robust SLAM, based on 4D radar, thermal camera, and IMU. Totally, the dataset is around 17.6km, 85mins, 50GB.

Introducing paper: NTU4DRadLM: 4D Radar-centric Multi-Modal Dataset for Localization and Mapping
AutoTherm

# Temporal Dataset for Indoor and In-Vehicle Thermal Comfort Estimation

## Abstract
Thermal 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. 

Our 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. 

## Key Contributions
- **Datasets**: Temporal multimodal datasets for indoor and in-vehicle thermal comfort estimation.
- **Machine Learning Models**: Comparative studies using recurrent architectures for state recognition and prediction.
- **Signal Importance**: Identification of key factors like ambient temperature, relative humidity, and skin response.
- **Benchmarking**: Evaluation against existing thermal comfort datasets.

## Keywords
Thermal comfort, temporal datasets, machine learning, recurrent neural networks, automotive research

Introducing paper: AutoTherm: A Dataset and Benchmark for Thermal Comfort Estimation Indoors and in Vehicles