Perception robustness under adverse weather remains a critical challenge for autonomous driving, with the core bottleneck being the scarcity of real-world video data in adverse weather. Existing weather generation approaches struggle to balance visual quality and annotation reusability. We present AutoAWG, a controllable Adverse Weather video Generation framework for Autonomous driving. Our method employs a semantics-guided adaptive fusion of multiple controls to balance strong weather stylization with high-fidelity preservation of safety-critical targets; leverages a vanishing point-anchored temporal synthesis strategy to construct training sequences from static images, thereby reducing reliance on synthetic data; and adopts masked training to enhance long-horizon generation stability. On the nuScenes validation set, AutoAWG significantly outperforms prior state-of-the-art methods: without first-frame conditioning, FID and FVD are relatively reduced by 50.0% and 16.1%; with first-frame conditioning, they are further reduced by 8.7% and 7.2%, respectively. Extensive qualitative and quantitative results demonstrate advantages in style fidelity, temporal consistency, and semantic--structural integrity, underscoring the practical value of AutoAWG for improving downstream perception in autonomous driving. Our code is available at: https://github.com/higherhu/AutoAWG

翻译：恶劣天气下的感知鲁棒性仍是自动驾驶的关键挑战，其核心瓶颈在于真实世界中恶劣天气视频数据的稀缺。现有天气生成方法难以在视觉质量与标注可复用性之间取得平衡。本文提出AutoAWG——一种面向自动驾驶的可控恶劣天气视频生成框架。该方法采用语义引导的自适应多控制融合策略，在强烈天气风格化与安全关键目标的高保真保留之间取得平衡；利用消失点锚定的时序合成策略，从静态图像构建训练序列，从而减少对合成数据的依赖；并采用掩码训练以增强长时程生成的稳定性。在nuScenes验证集上，AutoAWG显著优于先前最先进方法：无首帧条件时，FID和FVD分别相对降低50.0%和16.1%；采用首帧条件时，二者进一步降低8.7%和7.2%。大量定性与定量结果表明其在风格保真度、时序一致性及语义-结构完整性方面的优势，突显了AutoAWG在提升自动驾驶下游感知任务中的实用价值。我们的代码开源地址为：https://github.com/higherhu/AutoAWG