Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …

The Internet of Things (IoT) boom has revolutionized almost every corner of people's daily
lives: healthcare, environment, transportation, manufacturing, supply chain, and so on. With …

Graph neural networks (GNNs) have been extensively used in a wide variety of domains in
recent years. Owing to their power in analyzing graph-structured data, they have become …

Prediction of pedestrian behavior is critical for fully autonomous vehicles to drive in busy city
streets safely and efficiently. The future autonomous cars need to fit into mixed conditions …

Self-supervised monocular depth estimation has garnered significant attention in recent
years due to its practical value in applications, as it eliminates the need for ground truth …

The power of artificial intelligence of things (AIoT) stems from adapting machine learning
(ML) and artificial intelligence (AI) models into abundant intelligent IoT fields, based on a …

Navigating crowded environments with substantial pedestrian interactions poses distinctive
challenges for autonomous vehicles (AVs), primarily due to the interdependence of …

Pedestrian trajectory prediction is essential for self-driving vehicles, social robots, and
intelligent monitoring applications. Diverse trajectory data is critical for high-accuracy …

Reduced-scale mobile robots (RSMRs) are extensively used for studying autonomous
driving due to their ability to test models and algorithms in physical environments, their lack …

Predicting pedestrian motion trajectories is crucial for path planning and motion control of
autonomous vehicles. Accurately forecasting crowd trajectories is challenging due to the …