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 …

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 …

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

Predicting the future states of surrounding traffic participants and planning a safe, smooth,
and socially compliant trajectory accordingly are crucial for autonomous vehicles (AVs) …

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 …

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 …

Multi-agent trajectory forecasting in autonomous driving requires an agent to accurately
anticipate the behaviors of the surrounding vehicles and pedestrians, for safe and reliable …

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 …

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 …