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 …

Predicting diverse yet admissible trajectories that adhere to the map constraints is
challenging. Graph-based scene encoders have been proven effective for preserving local …

The ability to accurately predict the trajectory of surrounding vehicles is a critical hurdle to
overcome on the journey to fully autonomous vehicles. To address this challenge, we …

Predicting the future motion of road agents is a critical task in an autonomous driving
pipeline. In this work, we address the problem of generating a set of scene-level, or joint …

Deep motion forecasting models have achieved great success when trained on a massive
amount of data. Yet, they often perform poorly when training data is limited. To address this …

We present a novel framework to bootstrap Motion forecasting with Self-consistent
Constraints (MISC). The motion forecasting task aims at predicting future trajectories of …

To handle two shortcomings of existing methods,(i) nearly all models rely on the high-
definition (HD) maps, yet the map information is not always available in real traffic scenes …

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 …

For safe motion planning in real-world autonomous vehicles require behavior prediction
models that are reliable and robust to distribution shifts. The recent studies suggest that the …

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 …