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 …

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

It is critical to predict the motion of surrounding vehicles for self-driving planning, especially
in a socially compliant and flexible way. However, future prediction is challenging due to the …

Understanding the interaction between multiple agents is crucial for realistic vehicle
trajectory prediction. Existing methods have attempted to infer the interaction from the …

Autonomous vehicles navigate in dynamically changing environments under a wide variety
of conditions, being continuously influenced by surrounding objects. Mod-elling interactions …

Abstract We propose DROGON (Deep RObust Goal-Oriented trajectory prediction Network)
for accurate vehicle trajectory forecast by considering behavioral intention of vehicles in …

Predicting the trajectories of surrounding agents is an essential ability for autonomous
vehicles navigating through complex traffic scenes. The future trajectories of agents can be …

Recent advances in trajectory prediction have shown that explicit reasoning about agents'
intent is important to accurately forecast their motion. However, the current research …

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 …

Accurate prediction of others' trajectories is essential for autonomous driving. Trajectory
prediction is challenging because it requires reasoning about agents' past movements …