Generative End-to-End Autonomous Driving: Modeling Future Trajectory Distributions for Improved Motion Prediction and Planning

The proposed GenAD framework models autonomous driving as a generative problem, learning a structured latent space to capture the prior of realistic trajectories. This enables simultaneous motion prediction and planning by sampling from the learned distributions conditioned on the instance-centric scene representation.