Trajectory Forecasting

Trajectory forecasting aims to predict the future movement of objects, from pedestrians and vehicles to vital signs in healthcare, leveraging past observations and contextual information. Current research emphasizes improving prediction accuracy and diversity through advanced architectures like transformers, variational autoencoders, and graph neural networks, often incorporating kinematic models and handling uncertainty explicitly. This field is crucial for applications ranging from autonomous driving and robotics to personalized healthcare, enabling safer and more efficient systems by anticipating future events. The development of robust and efficient forecasting methods is driving progress across multiple disciplines.