Anticipating possible behaviors of traffic participants is an essential capability of
autonomous vehicles. Many behavior detection and maneuver recognition methods only …

Self-driving vehicles need to continuously analyse the driving scene, understand the
behavior of other road users and predict their future trajectories in order to plan a safe …

Predicting trajectories of multiple agents in interactive driving scenarios such as
intersections, and roundabouts are challenging due to the high density of agents, varying …

Vehicle-trajectory prediction is essential for intelligent traffic systems (ITS), as it can help
autonomous vehicles to plan a safe and efficient path. However, it is still a challenging task …

Self-supervised learning (SSL) is an emerging technique that has been successfully
employed to train convolutional neural networks (CNNs) and graph neural networks (GNNs) …

Trajectory generation and prediction are two in-terwoven tasks that play important roles in
planner evaluation and decision making for intelligent vehicles. Most existing methods focus …

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 …

Multimodal prediction results are essential for trajectory prediction task as there is no single
correct answer for the future. Previous frameworks can be divided into three categories …

Abstract We propose DROGON (Deep RObust Goal-Oriented trajectory prediction Network)
for accurate vehicle trajectory forecast by considering behavioral intention of vehicles in …

Forecasting the motion of surrounding obstacles (vehicles, bicycles, pedestrians and etc.)
benefits the on-road motion planning for intelligent and autonomous vehicles. Complex …