Multi Modal Trajectory Forecasting

Multi-modal trajectory forecasting aims to predict the likely future paths of moving agents (e.g., pedestrians, vehicles), acknowledging the inherent uncertainty and multiple possibilities. Current research emphasizes improving prediction accuracy and realism by incorporating social interactions, using advanced model architectures like variational autoencoders and transformer networks, and developing more robust evaluation metrics that consider the joint behavior of multiple agents. This field is crucial for advancing autonomous systems, particularly in robotics and self-driving cars, by enabling safer and more efficient navigation in complex, dynamic environments.