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 …

The indeterminate nature of human motion requires trajectory prediction systems to use a
probabilistic model to formulate the multi-modality phenomenon and infer a finite set of …

Abstract 3D point-clouds and 2D images are different visual representations of the physical
world. While human vision can understand both representations, computer vision models …

Multi-agent trajectory prediction is essential in autonomous driving risk avoidance and traffic
flow control. However the heterogeneous traffic density on interactions which caused by …

As the pretraining technique is growing in popularity, little work has been done on pretrained
learning-based motion prediction methods in autonomous driving. In this paper, we propose …

Accumulating substantial volumes of real-world driving data proves pivotal in the realm of
trajectory forecasting for autonomous driving. Given the heavy reliance of current trajectory …

Trajectory prediction is a challenging problem that requires considering interactions among
multiple actors and the surrounding environment. While data-driven approaches have been …

We introduce RedMotion, a transformer model for motion prediction in self-driving vehicles
that learns environment representations via redundancy reduction. Our first type of …

Inspired by recent developments regarding the application of self-supervised learning (SSL),
we devise an auxiliary task for trajectory prediction that takes advantage of map-only …

Due to the lack of depth cues in images, multi-frame inputs are important for the success of
vision-based perception, prediction, and planning in autonomous driving. Observations from …