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 …

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 the future motion of road participants is crucial for autonomous driving but is
extremely challenging due to staggering motion uncertainty. Recently, most motion …

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 …

We present MotionDiffuser, a diffusion based representation for the joint distribution of future
trajectories over multiple agents. Such representation has several key advantages: first, our …

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 …

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 …

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 …

Reliable forecasting of the future behavior of road agents is a critical component to safe
planning in autonomous vehicles. Here, we represent continuous trajectories as sequences …

Motion prediction is crucial for autonomous driving systems to understand complex driving
scenarios and make informed decisions. However, this task is challenging due to the diverse …