FogDrive: A Multi-Modal Synthetic Driving Dataset for Perception under Graded Fog
FogDrive is a multi-modal autonomous driving dataset built with the CARLA simulator, featuring 660 scenes (~133k frames) across four synchronized cameras, LiDAR, and radar. It models physically consistent fog at three visibility levels (160m, 100m, 50m) using Koschmieder's model for cameras and Beer-Lambert law for LiDAR, providing matched clean and foggy variants for each scene. A quality audit over 8k frames confirms 95.1% annotation precision and over 99% recall for vehicles within 40m. Baseline evaluations show that mixing multi-density fog during training improves 3D detection, while 2D image quality metrics are poor predictors of downstream performance.
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[Submitted on 18 Jul 2026]
Title:FogDrive: A Multi-Modal Synthetic Driving Dataset for Perception under Graded Fog
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Abstract:Perception under adverse weather remains a critical bottleneck for reliable autonomous driving, yet existing benchmarks lack the systematic multi-modal alignments needed to evaluate robust sensor fusion. Real-world weather datasets suffer from uncontrolled collection and single-level, uncalibrated conditions, while synthetic alternatives either target camera-only restoration or lack the paired clean-and-foggy structure needed to benchmark "defog-then-detect" pipelines. We present FogDrive, a rigorously calibrated, multi-modal autonomous-driving dataset bridging data-centric engineering and robust machine learning. Built with the CARLA simulator, FogDrive contains 660 scenes (~133k fully annotated frames, 50:50 day/night) across four synchronized cameras (RGB, depth, semantic segmentation), a LiDAR and semantic-LiDAR pair, and front radar. Physically consistent fog is modeled independently on camera channels (Koschmieder model) and LiDAR channels (Beer-Lambert law) at three calibrated visibility densities (160m, 100m, 50m). Every scene ships in four matched variants (clean plus three graded fog levels) with cross-calibrated 2D and 3D bounding boxes. A semantic-segmentation-based quality audit over 8k images validates annotations at 95.1% precision and over 99% recall for vehicles within 40m. We establish baseline benchmarks with state-of-the-art architectures (TransFusion, BEVFusion, YOLOv8-m) across two paradigms: 3D multi-modal fusion and 2D image restoration. These yield critical data-centric insights: mixing multi-density fog during training tightens 3D bounding-box geometry without added data-scaling cost, while in 2D pipelines image-quality metrics (PSNR, SSIM) prove poor predictors of downstream detection performance. FogDrive will be fully open-sourced alongside our data-generation framework to accelerate robust, multi-modal research.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.22698 [cs.CV]
(or arXiv:2607.22698v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.22698
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Vansh Panwar [view email] [v1] Sat, 18 Jul 2026 06:53:56 UTC (2,134 KB)
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