Self-supervised learning (SSL) is an emerging technique that has been successfully
employed to train convolutional neural networks (CNNs) and graph neural networks (GNNs) …

This study explores the application of self-supervised learning (SSL) to the task of motion
forecasting, an area that has not yet been extensively investigated despite the widespread …

Automated vehicles need to not only perceive their environment, but also predict the
possible future behavior of all detected traffic participants in order to safely navigate in …

Motion forecasting is a key module in an autonomous driving system. Due to the
heterogeneous nature of multi-sourced input, multimodality in agent behavior, and low …

Predicting the future trajectories of on-road vehicles is critical for autonomous driving. In this
paper, we introduce a novel prediction framework called PRIME, which stands for Prediction …

We present a novel framework for motion forecasting with Dual Consistency Constraints and
Multi-Pseudo-Target supervision. The motion forecasting task predicts future trajectories of …

To plan a safe and efficient route, an autonomous vehicle should anticipate future motions of
other agents around it. Motion prediction is an extremely challenging task that recently …

With the rapid development of machine learning, autonomous driving has become a hot
issue, making urgent demands for more intelligent perception and planning systems. Self …

Due to the complex and changing interactions in dynamic scenarios, motion forecasting is a
challenging problem in autonomous driving. Most existing works exploit static road graphs to …

Human trajectory forecasting is a key component of autonomous vehicles, social-aware
robots and advanced video-surveillance applications. This challenging task typically …