Pedestrian Data

Pedestrian data research focuses on understanding and predicting pedestrian behavior for applications like autonomous driving and urban planning. Current research emphasizes developing accurate and efficient models, often employing deep learning architectures such as transformers, graph neural networks, and convolutional neural networks, to analyze diverse data sources including video, sensor data, and even audio. This work is crucial for improving safety and efficiency in transportation systems, particularly by enhancing pedestrian detection, trajectory prediction, and the understanding of complex interactions between pedestrians and vehicles.