In recent years, the rapid advancement of deep learning and the advent of extensive human
motion datasets have significantly enhanced the prominence of human motion prediction …

In this paper, we present a new paradigm for evaluating egocentric pedestrian trajectory
prediction algorithms. Based on various contextual information, we extract driving scenarios …

Deep learning-based trajectory prediction models for autonomous driving often struggle with
generalization to out-of-distribution (OOD) scenarios, sometimes performing worse than …

Motion prediction has been an essential component of autonomous driving systems since it
handles highly uncertain and complex scenarios involving moving agents of different types …

Accurate prediction of whether pedestrians will cross the street is prevalently recognized as
an indispensable function of autonomous driving systems, especially in urban environments …

Deep reinforcement learning (DRL) provides a promising way for intelligent agents (eg,
autonomous vehicles) to learn to navigate complex scenarios. However, DRL with neural …

Reinforcement Learning (RL) has enabled social robots to generate trajectories without
human-designed rules or interventions, which makes it more effective than hard-coded …

Recent advancements in predicting pedestrian crossing intentions for Autonomous Vehicles
using Computer Vision and Deep Neural Networks are promising. However, the black-box …

Understanding and predicting human behaviors accurately are essential prerequisites for
effective human-robot interaction. Recently, there has been growing interest in multi-sensor …

Accurately predicting the crossing behaviour of pedestrians remains a significant challenge
due to their complex behavioural dynamics. Although modern transformer-based models …