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 …

Making safe and human-like decisions is an essential capability of autonomous driving
systems, and learning-based behavior planning presents a promising pathway toward …

Motion prediction and cost evaluation are vital components in the decision-making system of
autonomous vehicles. However, existing methods often ignore the importance of cost …

Predicting the behaviors of other road users is crucial to safe and intelligent decision-making
for autonomous vehicles (AVs). However, most motion prediction models ignore the …

Traffic simulation plays a crucial role in evaluating and improving autonomous driving
planning systems. After being deployed on public roads, autonomous vehicles need to …

Interacting with the surrounding road users is crucial for autonomous vehicles (AV).
However, the inherent multimodality and uncertainties associated with traffic participants …

We focus on the problem of how we can enable a robot to collaborate seamlessly with a
human partner, specifically in scenarios where preexisting data is sparse. Much prior work in …

Game theory offers an interpretable mathematical framework for modeling multi-agent
interactions. However, its applicability in real-world robotics applications is hindered by …

The growing adoption of autonomous vehicles (AVs) holds the promise of transforming
transportation systems, enhancing traffic safety, and supporting environmental sustainability …

Vehicle-to-everything technologies (V2X) have become an ideal paradigm to extend the
perception range and see through the occlusion. Exiting efforts focus on single-frame …