Scenario understanding and motion prediction are essential components for completely
replacing human drivers and for enabling highly and fully automated driving (SAE-Level …

Lately, Autonomous vehicles (AV) have been gaining traction globally owing to their huge
social, economic and environmental benefits. However, the rising safety apprehensions for …

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 …

Human behavior has the nature of indeterminacy, which requires the pedestrian trajectory
prediction system to model the multi-modality of future motion states. Unlike existing …

Better machine understanding of pedestrian behaviors enables faster progress in modeling
interactions between agents such as autonomous vehicles and humans. Pedestrian …

Human trajectory forecasting with multiple socially interacting agents is of critical importance
for autonomous navigation in human environments, eg, for self-driving cars and social …

Human trajectory forecasting is an inherently multimodal problem. Uncertainty in future
trajectories stems from two sources:(a) sources that are known to the agent but unknown to …

Trajectory prediction has been widely pursued in many fields, and many model-based and
model-free methods have been explored. The former include rule-based, geometric or …

Derived from rapid advances in computer vision and machine learning, video analysis tasks
have been moving from inferring the present state to predicting the future state. Vision-based …

Multi-agent interacting systems are prevalent in the world, from purely physical systems to
complicated social dynamic systems. In many applications, effective understanding of the …