Motion Predictor

Motion prediction aims to forecast the future movement of objects, primarily in autonomous driving and multi-object tracking applications. Current research focuses on improving the accuracy and robustness of predictions, particularly for non-linear and complex movements, employing diverse model architectures such as transformers, convolutional networks, diffusion models, and state-space models, often incorporating graph neural networks for handling interactions between objects. These advancements are crucial for enhancing the safety and efficiency of autonomous systems and improving the performance of computer vision tasks involving object tracking. The development of reliable and accurate motion prediction is a significant step towards more sophisticated and adaptable AI systems.