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 …

Predicting multiple plausible future trajectories of the nearby vehicles is crucial for the safety
of autonomous driving. Recent motion prediction approaches attempt to achieve such …

This paper proposes a novel deep learning framework for multi-modal motion prediction.
The framework consists of three parts: recurrent neural network to process target agent's …

Making accurate motion prediction of the surrounding traffic agents such as pedestrians,
vehicles, and cyclists is crucial for autonomous driving. Recent data-driven motion prediction …

Motion prediction is a crucial task in autonomous driving, and one of its major challenges
lands in the multimodality of future behaviors. Many successful works have utilized mixture …

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 for autonomous driving is a challenging task because complex driving
scenarios involve a heterogeneous mix of static and dynamic inputs. It is an open problem …

In this report, we present the 1st place solution for motion prediction track in 2022 Waymo
Open Dataset Challenges. We propose a novel Motion Transformer framework for …

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 which recently …

Due to the stochasticity of human behaviors, predicting the future trajectories of road agents
is challenging for autonomous driving. Recently, goal-based multi-trajectory prediction …