DiverseAD: A Large-Scale Driving Dataset with Diverse Atmospheric Conditions

The strong generalization capability to diversified atmospheric conditions is crucial for autonomous driving models. However, models trained on existing datasets struggle with real-world complexity due to the homogeneous weather, limited maneuvers, and insufficient scale in training data. To overcome this issue, we introduce DiverseAD, a largescale driving dataset comprising 150K scenes that feature a great diversity in atmospheric condition, road types, and driving actions. Based on this dataset, we further propose a novel end-to-end autonomous driving model robust across diverse atmospheric conditions. More specifically, an atmospheric-invariant feature learning mechanism is proposed, which spots and disentangles atmospheric-agnostic features from visual inputs by using driving intent and scene structure cues as stable anchors. Our method thus allows the extracted features to be more invariant to changes in atmospheric conditions. Experiments show that DiverseAD is superior to existing public datasets in diversity, hence is valuable for training and benchmarking. Extensive comparisons also illustrate the superior performance of our proposed method.