Goal Prediction

Goal prediction, the task of anticipating an agent's future intentions, is a rapidly developing field with applications ranging from human-robot collaboration to autonomous driving. Current research focuses on improving prediction accuracy and interpretability through various model architectures, including neural networks, temporal point processes, and graph neural networks, often incorporating elements like goal-conditioned prediction and path-based approaches to enhance performance and map compliance. These advancements are crucial for creating safer and more efficient systems in diverse domains, from improving human-computer interaction to enabling more robust autonomous navigation. The emphasis is on developing models that are not only accurate but also explainable and adaptable to various contexts and data types.