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

Predicting a vehicle's trajectory is an essential ability for autonomous vehicles navigating
through complex urban traffic scenes. Bird's-eye-view roadmap information provides …

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

Existing trajectory prediction methods for autonomous driving typically rely on one-stage
trajectory prediction models which condition future trajectories on observed trajectories …

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 …

Predicting the future trajectories of on-road vehicles is critical for autonomous driving. In this
paper, we introduce a novel prediction framework called PRIME, which stands for Prediction …

We present a new method for multi-modal, long-term vehicle trajectory prediction. Our
approach relies on using lane centerlines captured in rich maps of the environment to …

Vehicle trajectory prediction tasks have been commonly tackled from two distinct
perspectives: either with knowledge-driven methods or more recently with data-driven ones …

In this paper, we present an online two-level vehicle trajectory prediction framework for
urban autonomous driving where there are complex contextual factors, such as lane …

We introduce a motion forecasting (behavior prediction) method that meets the latency
requirements for autonomous driving in dense urban environments without sacrificing …