Autonomous vehicles (AVs) have the potential to significantly revolutionize society by
providing a secure and efficient mode of transportation. Recent years have witnessed …

Obtaining accurate and diverse human motion prediction is essential to many industrial
applications, especially robotics and autonomous driving. Recent research has explored …

Deep learning has recently achieved significant progress in trajectory forecasting. However,
the scarcity of trajectory data inhibits the data-hungry deep-learning models from learning …

Contemporary attention-based sequential recommendations often encounter the
oversmoothing problem, which generates indistinguishable representations. Although …

Motion forecasting in highly interactive scenarios is a challenging problem in autonomous
driving. In such scenarios, we need to accurately predict the joint behavior of interacting …

Conditional behavior prediction (CBP) builds up the foundation for a coherent interactive
prediction and plan-ning framework that can enable more efficient and less conser-vative …

In recent years, the pervasive deployment and progression of autonomous driving
technology have engendered heightened demands, particularly within the intricate campus …

The design of a safe and reliable Autonomous Driving stack (ADS) is one of the most
challenging tasks of our era. These ADS are expected to be driven in highly dynamic …

Motion prediction (MP) of multiple agents is a crucial task in arbitrarily complex
environments, from social robots to self-driving cars. Current approaches tackle this problem …

Explainability is essential for autonomous vehicles and other robotics systems interacting
with humans and other objects during operation. Humans need to understand and anticipate …