Multimodal Future Trajectory

Multimodal future trajectory prediction aims to accurately forecast the multiple possible future paths of moving agents, such as vehicles or pedestrians, in dynamic environments. Current research heavily utilizes transformer-based architectures, often incorporating graph neural networks and diffusion models, to capture complex interactions and contextual information, including road maps and agent-agent relationships, for improved prediction accuracy and realism. This field is crucial for advancements in autonomous driving, robotics, and human-computer interaction, enabling safer and more efficient systems by providing robust predictions of future agent behavior. The development of more efficient and interpretable models remains a key focus.