The open road poses many challenges to autonomous perception, including poor visibility from extreme weather conditions. Models trained on good-weather datasets frequently fail at detection in these out-of-distribution settings. To aid adversarial robustness in perception, we introduce WEDGE (WEather images by DALL-E GEneration): a synthetic dataset generated with a vision-language generative model via prompting. WEDGE consists of 3360 images in 16 extreme weather conditions manually annotated with 16513 bounding boxes, supporting research in the tasks of weather classification and 2D object detection. We have analyzed WEDGE from research standpoints, verifying its effectiveness for extreme-weather autonomous perception. We establish baseline performance for classification and detection with 53.87% test accuracy and 45.41 mAP. Most importantly, WEDGE can be used to fine-tune state-of-the-art detectors, improving SOTA performance on real-world weather benchmarks (such as DAWN) by 4.48 AP for well-generated classes like trucks. WEDGE has been collected under OpenAI's terms of use and is released for public use under the CC BY-NC-SA 4.0 license. The repository for this work and dataset is available at https://infernolia.github.io/WEDGE.

翻译：开放道路给自动驾驶感知带来了诸多挑战，包括极端天气条件下能见度低的问题。在良好天气数据集上训练的模型通常难以在分布外场景中完成检测任务。为增强感知的对抗鲁棒性，我们提出了WEDGE（通过DALL-E生成的天气图像）：一个通过提示词使用视觉语言生成模型生成的合成数据集。WEDGE包含16种极端天气条件下的3360张图像，并手工标注了16513个边界框，支持天气分类和二维目标检测任务的研究。我们从研究角度对WEDGE进行了分析，验证了其在极端天气自动驾驶感知中的有效性。我们建立了分类和检测任务的基线性能，测试准确率为53.87%，平均精度均值（mAP）为45.41%。最重要的是，WEDGE可用于微调最先进的检测器，在真实世界天气基准（如DAWN）上，对卡车等生成质量较好的类别，将最先进性能（SOTA）提升了4.48 AP。WEDGE遵循OpenAI的使用条款收集，并依据CC BY-NC-SA 4.0许可证向公众开放使用。本工作及数据集仓库见https://infernolia.github.io/WEDGE。