FrozenDrive: Zero-Shot Text-Guided Driving Scene Generation and Data Augmentation with Parameter-Free Frozen Diffusion Model

Synthetic data for autonomous driving is surging, poweredby diffusion models that promise scalable scene generation. Yet key ob-stacles remain, since multi-view and temporal consistency often requiresbackbone fine-tuning or added layers, which can erode pretrained knowl-edge and weaken text alignment. Models also stay close to the train-ing distribution, struggling under adverse weather and unseen configu-rations, while fidelity favors frequent over rare classes. We address thesegaps with FrozenDrive, a controllable generative framework that pre-serves pretrained diffusion knowledge while achieving strong consistency.FrozenDrive conditions on driving-stack signals and text prompts, andexpands the context of frozen self-attention across views and frames topromote cross-view alignment and temporal coherence in one pass, with-out trainable spatio-temporal modules in the diffusion backbone. Anobject-focused constraint further improves fidelity for rare categories.Without weather- or scene-specific fine-tuning, FrozenDrive synthesizesglobally coherent multi-view driving scenes from text and surpasses priorbaselines under adverse and rare conditions. On nuScenes, FrozenDrive-augmented data improves AD model performance, especially at night andin rain, demonstrating strong robustness with scenario-targeted data.