Graph Based Trajectory

Graph-based trajectory prediction focuses on forecasting the future movements of objects, particularly in complex scenarios like autonomous driving, by representing their interactions as a graph. Current research emphasizes using graph neural networks (GNNs), often incorporating cooperative information from connected vehicles and employing differentially constrained motion models to ensure physically realistic predictions. This field is crucial for advancing autonomous systems and robotics, improving safety and efficiency through more accurate and robust trajectory forecasting.