Navigating efficiently and safely through human crowds is essential for mobile robots in
diverse applications such as delivery services, home assistance, healthcare, and …

Autonomous navigation in crowded spaces poses a challenge for mobile robots due to the
highly dynamic, partially observable environment. Occlusions are highly prevalent in such …

Accurate prediction of others' trajectories is essential for autonomous driving. Trajectory
prediction is challenging because it requires reasoning about agents' past movements …

For a mobile robot, navigation skills that are safe, efficient, and socially compliant in
crowded, dynamic environments are essential. This is a particularly challenging problem as …

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 in urban scenarios is essential for automated driving. This
task is challenging because the behavior of pedestrians is influenced by both their own …

Learning to understand human behaviors and predict their trajectories is a prerequisite for
an automated car to navigate through the crowd safely and efficiently. This problem is …

This paper proposes a multiple stakeholder perspective model (MSPM) which predicts the
future pedestrian trajectory observed from vehicle's point of view. For the vehicle-pedestrian …

In densely populated environments, socially compliant navigation is critical for autonomous
robots as driving close to people is unavoidable. This manner of social navigation is …

Autonomous driving in an unregulated urban crowd is an outstanding challenge, especially,
in the presence of many aggressive, high-speed traffic participants. This paper presents …