Generative Planning for Autonomous Driving

Compare the traditional modular AD pipeline (perception, prediction, planning) with unified generative planning

Explain how trajectory planning can be reformulated as an autoregressive sequence modeling task

Describe VQ-VAE: the encoder, learned codebook, nearest-neighbor quantization, and decoder

Explain how VQ-VAE tokenizes Bird's Eye View (BEV) feature maps into discrete scene tokens

Describe DAP's tokenization scheme for ego history (curvature/acceleration), navigation commands, and environment

Explain DAP's autoregressive interleaving: predicting future scene tokens before generating ego action tokens

Describe how predicting the next scene state before acting effectively embeds a world model inside the planner

Explain post-tuning refinement that smooths discrete-token trajectories via jerk minimization