In order to plan a safe maneuver an autonomous vehicle must accurately perceive its
environment, and understand the interactions among traffic participants. In this paper, we …

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

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 address one of the crucial aspects necessary for safe and efficient operations of
autonomous vehicles, namely predicting future state of traffic actors in the autonomous …

Forecasting the long-term future motion of road actors is a core challenge to the deployment
of safe autonomous vehicles (AVs). Viable solutions must account for both the static …

Roads have well defined geometries, topologies, and traffic rules. While this has been
widely exploited in motion planning methods to produce maneuvers that obey the law, little …

Perception and prediction are two separate modules in the existing autonomous driving
systems. They interact with each other via hand-picked features such as agent bounding …

Trajectory prediction is critical for applications of planning safe future movements and
remains challenging even for the next few seconds in urban mixed traffic. How an agent …

Forecasting the motion of surrounding obstacles (vehicles, bicycles, pedestrians and etc.)
benefits the on-road motion planning for intelligent and autonomous vehicles. Complex …

Predicting future motions of road participants is an important task for driving autonomously in
urban scenes. Existing models excel at predicting marginal trajectories for single agents, yet …