To enable intelligent automated driving systems, a promising strategy is to understand how
human drives and interacts with road users in complicated driving situations. In this paper …

Modeling interactions among vehicles is critical in improving the efficiency and safety of
autonomous driving since complex interactions are ubiquitous in many traffic scenarios. To …

Behavior prediction of traffic actors is an essential component of any real-world self-driving
system. Actors' long-term behaviors tend to be governed by their interactions with other …

Collision risk assessment aims to provide a subjective cognitive comprehension of the risk
level in driving scenarios, which is critical for the safety of autonomous driving systems …

How to make precise multi-agent trajectory prediction is a crucial problem in the context of
autonomous driving. It is significant to have the ability to predict surrounding road …

We present HOI4D, a large-scale 4D egocentric dataset with rich annotations, to catalyze the
research of category-level human-object interaction. HOI4D consists of 2.4 M RGB-D …

We present a novel Multi-Relational Graph Convolutional Network (MRGCN) based
framework to model on-road vehicle behaviors from a sequence of temporally ordered …

In this work, we aim to predict the future motion of vehicles in a traffic scene by explicitly
modeling their pairwise interactions. Specifically, we propose a graph neural network that …

Egocentric interaction recognition aims to recognize the camera wearer's interactions with
the interactor who faces the camera wearer in egocentric videos. In such a human-human …

Despite the advancement in the technology of autonomous driving cars, the safety of a self-
driving car is still a challenging problem that has not been well studied. Motion prediction is …