No human drives a car in a vacuum; she/he must negotiate with other road users to achieve
their goals in social traffic scenes. A rational human driver can interact with other road users …

Autonomous vehicles operating in complex real-world environments require accurate
predictions of interactive behaviors between traffic participants. This paper tackles the …

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

The real-world deployment of an autonomous driving system requires its components to run
on-board and in real-time, including the motion prediction module that predicts the future …

Abstract Online vectorized High-Definition (HD) map construction is critical for downstream
prediction and planning. Recent efforts have built strong baselines for this task, however …

Motion prediction modules are crucial for autonomous vehicles to forecast the future
behavior of surrounding road users. Failures in prediction modules can mislead a …

Data-driven simulation has become a favorable way to train and test autonomous driving
algorithms. The idea of replacing the actual environment with a learned simulator has also …

Autonomous driving requires accurate local scene understanding information. To this end,
autonomous agents deploy object detection and online BEV lane graph extraction methods …

Trajectory prediction is an integral component of modern autonomous systems as it allows
for envisioning future intentions of nearby moving agents. Due to the lack of other agents' …

Seamless human-robot manipulation in close proximity relies on accurate forecasts of
human motion. While there has been significant progress in learning forecast models at …