Understanding and forecasting future trajectories of agents are critical for behavior analysis,
robot navigation, autonomous cars, and other related applications. Previous methods mostly …

Robust multi-agent trajectory prediction is essential for the safe control of robotic systems. A
major challenge is to efficiently learn a representation that approximates the true joint …

Trajectory prediction is crucial for autonomous vehicles. The planning system not only needs
to know the current state of the surrounding objects but also their possible states in the …

Data-driven simulation has become a favorable way to train and test autonomous driving
algorithms. The idea of replacing the actual environment with a learned simulator has also …

Pedestrian trajectory prediction is an essential component in a wide range of AI applications
such as autonomous driving and robotics. Existing methods usually assume the training and …

The reasonable trajectory prediction of surrounding traffic participants is crucial for
autonomous driving. Especially, how to predict multiple plausible trajectories is still a …

This paper tackles video prediction from a new dimension of predicting spacetime-varying
motions that are incessantly changing across both space and time. Prior methods mainly …

To accurately predict future positions of different agents in traffic scenarios is crucial for
safely deploying intelligent autonomous systems in the real-world environment. However, it …

In this paper, we propose THOMAS, a joint multi-agent trajectory prediction framework
allowing for an efficient and consistent prediction of multi-agent multi-modal trajectories. We …

Contrast maximisation estimates the motion captured in an event stream by maximising the
sharpness of the motion-compensated event image. To carry out contrast maximisation …