Intention Prediction

Intention prediction focuses on anticipating the actions of agents, such as drivers or pedestrians, by analyzing their behavior and context. Current research heavily utilizes deep learning models, particularly transformer-based architectures and recurrent neural networks, often incorporating multi-task learning and federated learning techniques to improve accuracy and address privacy concerns. This field is crucial for enhancing safety and efficiency in autonomous driving systems and human-AI collaboration, with applications ranging from improving traffic flow to creating more effective digital health interventions. The development of robust and generalizable intention prediction models is a significant area of ongoing investigation.