Trajectory Prediction Model

Trajectory prediction models aim to forecast the future movements of agents (e.g., vehicles, pedestrians) in dynamic environments, primarily to enhance safety and efficiency in autonomous systems. Current research emphasizes improving model robustness and generalization across diverse scenarios, often employing deep learning architectures like transformers and graph convolutional networks, along with techniques such as multimodal learning and uncertainty quantification. These advancements are crucial for reliable autonomous navigation, human-robot interaction, and traffic simulation, impacting both the development of safer autonomous vehicles and a deeper understanding of agent behavior in complex systems.