In crowd scenarios, predicting trajectories of pedestrians is a complex and challenging task
depending on many external factors. The topology of the scene and the interactions …

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 …

We propose to predict the future trajectories of observed agents (eg, pedestrians or vehicles)
by estimating and using their goals at multiple time scales. We argue that the goal of a …

There are five types of trajectory prediction tasks: deterministic stochastic domain adaptation
momentary observation and few-shot. These associated tasks are defined by various factors …

Abstract Language models have demonstrated impressive ability in context understanding
and generative performance. Inspired by the recent success of language foundation models …

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

Predicting the trajectories of pedestrians in crowded conditions is an important task for
applications like autonomous navigation systems. Previous studies have tackled this …

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 …

Modeling the dynamics of people walking is a problem of long-standing interest in computer
vision. Many previous works involving pedestrian trajectory prediction define a particular set …

Pedestrian trajectory prediction is a classic and challenging scientific task that involves
complex engineering science and human factors. These challenges have spurred a …