Human trajectory forecasting in crowds, at its core, is a sequence prediction problem with
specific challenges of capturing inter-sequence dependencies (social interactions) and …

Since the past few decades, human trajectory forecasting has been a field of active research
owing to its numerous real-world applications: evacuation situation analysis, deployment of …

Human trajectory prediction is challenging and critical in various applications (eg,
autonomous vehicles and social robots). Because of the continuity and foresight of the …

Pedestrian trajectory prediction is a challenging task because of the complexity of real-world
human social behaviors and uncertainty of the future motion. For the first issue, existing …

Mimicking human ability to forecast future positions or interpret complex interactions in
urban scenarios, such as streets, shopping malls or squares, is essential to develop socially …

Capturing multimodal natures is essential for stochastic pedestrian trajectory prediction, to
infer a finite set of future trajectories. The inferred trajectories are based on observation …

Forecasting human trajectories in complex dynamic environments plays a critical role in
autonomous vehicles and intelligent robots. Most existing methods learn to predict future …

Understanding and predicting human motion behavior with social interactions have become
an increasingly crucial problem for a vast number of applications, ranging from visual …

Better machine understanding of pedestrian behaviors enables faster progress in modeling
interactions between agents such as autonomous vehicles and humans. Pedestrian …

Capturing high-dimensional social interactions and feasible futures is essential for
predicting trajectories. To address this complex nature, several attempts have been devoted …