With growing numbers of intelligent autonomous systems in human environments, the ability
of such systems to perceive, understand, and anticipate human behavior becomes …

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 …

Develo** a safe and efficient collision-avoidance policy for multiple robots is challenging
in the decentralized scenarios where each robot generates its paths with limited observation …

For robotic vehicles to navigate safely and efficiently in pedestrian-rich environments, it is
important to model subtle human behaviors and navigation rules (eg, passing on the right) …

Pedestrian trajectory prediction is an extremely challenging problem because of the
crowdedness and clutter of the scenes. Previous deep learning LSTM-based approaches …

Finding feasible, collision-free paths for multiagent systems can be challenging, particularly
in non-communicating scenarios where each agent's intent (eg goal) is unobservable to the …

Intelligent systems are increasingly part of our everyday lives and have been integrated
seamlessly to the point where it is difficult to imagine a world without them. Physical …

The recent progress in autonomous vehicle research and development has led to
increasingly widespread testing of fully autonomous vehicles on public roads, where …

Pedestrian trajectory prediction is vital for transportation systems. Generally we can divide
pedestrian behavior modeling into two categories, ie, knowledge-driven and data-driven …

Safe and efficient crowd navigation for mobile robot is a crucial yet challenging task.
Previous work has shown the power of deep reinforcement learning frameworks to train …