Do Better Visual Representations Always Lead to Better End-to-End Autonomous Driving?
This study addresses the unclear conditions for effectively applying vision foundation models (VFMs) in end-to-end autonomous driving. We propose ViRA, a framework that systematically investigates how VFM representations influence driving performance, revealing the critical roles of target selection and supervision strategies. The method enables efficient training through planner-agnostic visual representation alignment combined with diffusion models. Furthermore, it demonstrates that auxiliary perceptual supervision significantly enhances robustness and compensates for deficiencies arising from suboptimal target selection. Experimental results show that ViRA-Diffusion achieves an EPDMS of 92.3 on the NAVSIM v2 benchmark, outperforming comparable methods by at least 1.9 points.