Predicting the motion of multiple agents is necessary for planning in dynamic environments.
This task is challenging for autonomous driving since agents (eg vehicles and pedestrians) …

Diverse transport demands have resulted in the wide existence of heterogeneous vehicle
automation systems. While these systems have demonstrated effectiveness, they also pose …

Facial Expression Recognition (FER) in the wild is extremely challenging due to occlusions,
variant head poses, face deformation and motion blur under unconstrained conditions …

Despite the advancements in the technologies of autonomous driving, it is still challenging to
study the safety of a self-driving vehicle. Trajectory prediction is one core function of an …

For autonomous vehicles driving on roads, future trajectories of surrounding traffic agents
(eg, vehicles, bicycles, pedestrians) are essential information. The prediction of future …

Abstract We propose\textit {JFP}, a Joint Future Prediction model that can learn to generate
accurate and consistent multi-agent future trajectories. For this task, many different methods …

Automated vehicles are expected to navigate complex urban environments safely along with
several non-cooperating agents. Therefore, accurate trajectory prediction is crucial for safe …

Multi-agent trajectory prediction is critical for planning and decision-making in human-
interactive autonomous systems, such as self-driving cars. However, most prediction models …

Fast and accurate long-term trajectory prediction of surrounding vehicles (SVs) is critical to
autonomous driving systems. In high-density traffic flows, strongly correlated vehicle …

Vehicle Trajectory Prediction (VTP) is one of the key issues in the field of autonomous
driving. In recent years, more researchers have tried applying Deep Learning methods and …