Heterogeneous Trajectory

Heterogeneous trajectory forecasting focuses on predicting the future paths of diverse moving objects (vehicles, pedestrians, robots, etc.), which differ in their movement patterns and representation (e.g., 2D/3D coordinates, bounding boxes). Current research emphasizes developing robust models that handle these variations, often employing graph convolutional networks, hierarchical architectures, and spectral methods to capture complex interactions and temporal dependencies within and between trajectories. These advancements are crucial for applications like autonomous driving, robotics, and traffic management, enabling safer and more efficient systems by improving prediction accuracy and uncertainty quantification.