Long Term Trajectory Prediction

Long-term trajectory prediction aims to forecast the future paths of moving entities (vehicles, pedestrians, etc.) over extended time horizons, a crucial task for autonomous systems and traffic management. Current research emphasizes addressing the challenges of accumulated errors and data imbalance (the "long-tail" problem), focusing on model architectures that incorporate contextual information (e.g., scene layout, social interactions), physics-based constraints, and multi-modal predictions. These advancements improve prediction accuracy and robustness, particularly in complex or less-frequently observed scenarios, leading to safer and more efficient autonomous systems and improved understanding of human and vehicle movement patterns.