# ViRA-Diffusion

The authors introduce ViRA, a planner-agnostic visual representation alignment framework that supervises the planner encoder with frozen visual foundation model (VFM) intermediate features during training and discards the alignment modules at inference, leaving planner architecture and inference cost unchanged; across NAVSIM v2 and HUGSIM, ViRA consistently improves driving performance for regression-based, diffusion-based, and scoring-based planners, with gains depending on the VFM target and on whether the planner uses auxiliary perception supervision.

The authors introduce ViRA, a planner-agnostic visual representation alignment framework that supervises the planner encoder with frozen visual foundation model (VFM) intermediate features during training and discards the alignment modules at inference, leaving planner architecture and inference cost unchanged; across NAVSIM v2 and HUGSIM, ViRA consistently improves driving performance for regression-based, diffusion-based, and scoring-based planners, with gains depending on the VFM target and on whether the planner uses auxiliary perception supervision.