From Likelihood to Utility: Quality-Axis Diffusion for End-to-End Autonomous Driving

Diffusion models have become a dominant paradigm for end-to-end autonomous driving, yet existing planners organize trajectory generation along the Gaussian-noise axis, treating driving utility only as an auxiliary guidance or selection signal.

We instead formulate diffusion along a driving-quality axis.

We propose QualiDrive, which constructs scene-specific quality ladders by ranking observed trajectories according to measured driving utility and learns to reverse this ordered degradation through ordered low-to-high-quality refinement.

Each reverse step is conditioned on the scene, ego state, current trajectory, and target quality level, progressively refining trajectories toward higher driving quality.

On NAVSIM, QualiDrive achieves 94.5 PDMS on v1 and a state-of-the-art 92.74 EPDMS on v2.

QualiDrive improves Oracle EPDMS only modestly over endpoint-only quality supervision (98.52 to 98.57), but substantially increases the fractions of candidates above 0.95 and 0.98 EPDMS from 12.94%/9.71% to 21.53%/15.93%, raising selected EPDMS from 92.63 to 92.74.

These results suggest that diffusion is not merely a process of denoising, but one of progressive quality-ordered refinement.