Scenario understanding and motion prediction are essential components for completely
replacing human drivers and for enabling highly and fully automated driving (SAE-Level …

In this paper, we propose GOHOME, a method leveraging graph representations of the High
Definition Map and sparse projections to generate a heatmap output representing the future …

Accurately predicting the future motion of surrounding vehicles requires reasoning about the
inherent uncertainty in driving behavior. This uncertainty can be loosely decoupled into …

In this paper, we propose HOME, a framework tackling the motion forecasting problem with
an image output representing the probability distribution of the agent's future location. This …

In this paper, we propose THOMAS, a joint multi-agent trajectory prediction framework
allowing for an efficient and consistent prediction of multi-agent multi-modal trajectories. We …

Vehicle trajectory prediction is a keystone for the application of the internet of vehicles (IoV).
With the help of deep learning and big data, it is possible to understand the between-vehicle …

In this paper, we address the problem of predicting the future motion of a dynamic agent
(called a target agent) given its current and past states as well as the information on its …

We address the problem of forecasting pedestrian and vehicle trajectories in unknown
environments, conditioned on their past motion and scene structure. Trajectory forecasting is …

The reasonable trajectory prediction of surrounding traffic participants is crucial for
autonomous driving. Especially, how to predict multiple plausible trajectories is still a …

As a core function of autonomous driving (AD) and the Internet of Vehicles (IoV), accurately
predicting the trajectory of vehicles can significantly improve traffic safety and reduce crash …