Risk-Informed Diffusion Transformer for Long-Tail Trajectory Prediction in the Crash Scenario

Understand

Trajectory prediction methods have been widely applied in autonomous driving technologies.

• Although the overall performance accuracy of trajectory prediction is relatively high, the lack of trajectory data in critical scenarios in the training data leads to the long-tail phenomenon.
• Normally, the trajectories of the tail data are more critical and more difficult to predict and may include rare scenarios such as crashes.
• To solve this problem, we extracted the trajectory data from real-world crash scenarios, which contain more long-tail data.