Communication Dataset

Communication datasets are becoming increasingly crucial for advancing research in areas like autonomous driving and human-computer interaction. Current research focuses on creating large-scale, multi-modal datasets capturing diverse real-world scenarios, including vehicle-to-vehicle communication at high frequencies and human dyadic interactions involving audio, visual, and textual data. These datasets, often incorporating detailed annotations and diverse sensor modalities, are enabling the development and evaluation of novel machine learning models, such as transformer-based architectures and VQ-VAEs, for improved communication systems and a deeper understanding of human communication dynamics. The resulting insights have significant implications for developing safer and more efficient transportation systems and more realistic and intuitive human-computer interfaces.