From Representational Complementarity to Dual Systems: Synergizing VLM and Vision-Only Backbones for End-to-End Driving

Vision-Language-Action (VLA) driving augments end-to-end (E2E) planning with language-enabled visual backbones, but how VLM representations differ from vision-only encoders after policy learning, and whether such differences matter for planning, remains unclear. Under a unified VLM-hidden + diffusion-policy paradigm, we compare multiple VLM families/scales (InternVL3 and Qwen3VL) with standard vision-only encoders (ResNet, ViT, and EVA-CLIP) while keeping the downstream planner fixed. We study representation, behavior, and system design. CKA/CCA and Shared--Unique SAE show that policy learning enlarges a common decision subspace, but both branches retain non-transferable residual factors. Latent-intervention policies and scenario-level analysis further show that these residuals are behaviorally meaningful: vision-only encoders are stronger in simple geometry-dominant scenes, whereas VLMs are more effective in semantically complex and interaction-heavy long-tail cases. The two branches also exhibit distinct progress--braking and path-choice tendencies, and an oracle best-of-two VLM+ViT selector reaches 93.58 PDMS on NAVSIM. We convert this complementarity into two lightweight systems: HybridDriveVLA, which selects from a compact cross-model candidate set using a learned trajectory scorer and improves PDMS from 90.80 to 92.10, and DualDriveVLA, a fast--slow variant that invokes the VLM in only 15\% of scenarios, achieving 91.00 PDMS with about $1.9\times$ lower latency than the VLM baseline. Code will be released.