Future Trajectory

Predicting future trajectories—the paths of objects or agents over time—is a crucial area of research with applications ranging from autonomous driving to healthcare. Current efforts focus on developing sophisticated models, often employing deep learning architectures like transformers and graph neural networks, to accurately forecast trajectories, incorporating factors such as social interactions, environmental context, and individual agent characteristics. These advancements aim to improve the safety and efficiency of autonomous systems, enhance healthcare decision-making, and provide more robust predictions in various dynamic environments. The field is actively exploring probabilistic methods to quantify uncertainty in predictions and improve model interpretability.