Motion Forecasting

Motion forecasting aims to predict the future trajectories of moving agents, such as vehicles and pedestrians, a crucial task for autonomous driving and robotics. Current research heavily focuses on improving prediction accuracy and robustness, particularly in complex, multi-agent scenarios, employing diverse model architectures including transformers, diffusion models, and recurrent neural networks, often enhanced by techniques like model ensembling and self-supervised pre-training. These advancements are driven by the need for safer and more efficient autonomous systems, with significant implications for improving the reliability and performance of autonomous vehicles and human-robot interaction. Furthermore, the development of standardized datasets and evaluation metrics is actively pursued to facilitate more robust comparisons and accelerate progress in the field.