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

Learning-based driving solution, a new branch for autonomous driving, is expected to
simplify the modeling of driving by learning the underlying mechanisms from data. To …

Accurate trajectory prediction of surrounding vehicles is vital for automated vehicles to
achieve high-level driving safety in complex situations. However, most state-of-the-art …

Vehicle trajectory prediction has been an active research area in autonomous driving. In a
real traffic scene, autonomous vehicle needs to predict future motion of surrounding vehicles …

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

The prediction of surrounding agent trajectories in heterogeneous traffic environments
remains a challenging task for autonomous driving due to several critical issues, such as …

Autonomous driving presents one of the largest problems that the robotics and artificial
intelligence communities are facing at the moment, both in terms of difficulty and potential …

Integrating trajectory prediction to the decision-making and planning modules of modular
autonomous driving systems is expected to improve the safety and efficiency of self-driving …

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

Automated vehicles and advanced driver-assistance systems require an accurate prediction
of future traffic scene states. The tendency in recent years has been to use deep learning …