Trajectory Corner Case

Trajectory corner cases represent unusual or unexpected movement patterns in datasets used to train and evaluate models for tasks like autonomous driving and human mobility analysis. Current research focuses on developing robust models capable of handling these cases, employing techniques like diffusion models, autoencoders, and recurrent neural networks to generate, predict, and interpret diverse trajectories, often incorporating contextual information such as road networks or driver behavior. Addressing trajectory corner cases is crucial for improving the safety and reliability of autonomous systems and enhancing the accuracy of predictive models in various applications, particularly where accurate prediction of human or vehicle behavior is critical.