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 …

Long short-term memory (LSTM) has transformed both machine learning and
neurocomputing fields. According to several online sources, this model has improved …

In order to drive safely in a dynamic environment, autonomous vehicles should be able to
predict the future states of traffic participants nearby, especially surrounding vehicles, similar …

Predicting the future trajectories of surrounding agents is essential for autonomous vehicles
to operate safely. This paper presents QCNet, a modeling framework toward pushing the …

Predicting multimodal future behavior of traffic participants is essential for robotic vehicles to
make safe decisions. Existing works explore to directly predict future trajectories based on …

Accurately predicting the future motions of surrounding traffic agents is critical for the safety
of autonomous vehicles. Recently, vectorized approaches have dominated the motion …

As autonomous driving systems mature, motion forecasting has received increasing
attention as a critical requirement for planning. Of particular importance are interactive …

Motion forecasting for autonomous driving is a challenging task because complex driving
scenarios involve a heterogeneous mix of static and dynamic inputs. It is an open problem …

Predicting accurate future trajectories of multiple agents is essential for autonomous systems
but is challenging due to the complex interaction between agents and the uncertainty in …

The challenging task of multi-object tracking (MOT) requires simultaneous reasoning about
track initialization, identity, and spatio-temporal trajectories. We formulate this task as a …