Multi-Modal Trajectory Prediction & Imitation Learning
Obstacle trajectory prediction is crucial for modern autonomous vehicles. In this project, we developed multi-modal trajectory prediction. That is, the deep learning model can predict multiple possible trajectories and their corresponding pobabilities, considering map, topology and surrounding agents’ motions. Those information are very useful for downstream control & planning module.
I also also design an archor-based imitation learning algorithm which learns from drivers’ demonstrations and can automatically generate multiple possible trajectories with diffusion model, given anchor points and driver’s historic motions. The planned trajectory can be tracked by downstream control module. In practice, we can sample future points on centerlines as anchor points.


