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 …

An effective understanding of the environment and accurate trajectory prediction of
surrounding dynamic obstacles are indispensable for intelligent mobile systems (eg …

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

Motion forecasting plays a significant role in various domains (eg, autonomous driving,
human-robot interaction), which aims to predict future motion sequences given a set of …

Enabled by advanced data analytics and intelligent computing, augmented reality head-up
displays (AR-HUDs) are appraised with a certain degree of intelligence towards an in-car …

Multi-agent trajectory prediction plays a crucial role in robotics and autonomous driving. The
current mainstream research focuses on how to achieve accurate prediction on one large …

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 …

Reinforcement learning (RL) is renowned for its proficiency in modeling sequential tasks
and adaptively learning latent data patterns. Deep learning models have been extensively …

Accurate identification of important objects in the scene is a prerequisite for safe and high-
quality decision making and motion planning of intelligent agents (eg, autonomous vehicles) …

In this work, we present a novel multi-modal trajectory prediction architecture. We
decompose the uncertainty of future trajectories along higher-level scene characteristics and …