Multi Modal Trajectory Prediction

Multimodal trajectory prediction aims to forecast the likely future paths of moving agents (e.g., vehicles, pedestrians) by considering the inherent uncertainty in their movements. Current research heavily utilizes deep learning architectures, such as transformers, variational autoencoders, and graph neural networks, often incorporating contextual information from maps and social interactions to improve prediction accuracy and realism. This field is crucial for advancing autonomous systems, particularly in robotics and self-driving cars, by enabling safer and more efficient navigation through better anticipation of agent behavior and collision avoidance. Furthermore, research also addresses the security vulnerabilities of these prediction models to adversarial attacks.