FogDrive: A Multi-Modal Synthetic Driving Dataset for Perception under Graded Fog
Quick Answer
FogDrive is a new multi-modal synthetic driving dataset designed for evaluating perception in foggy conditions, featuring 660 scenes and 133k annotated frames from synchronized cameras and LiDAR.
Quick Take
It establishes benchmarks using architectures like TransFusion and YOLOv8-m, revealing that mixed fog training improves 3D bounding box accuracy without extra data costs.
Key Points
- FogDrive includes 660 scenes with 133k fully annotated frames across multiple sensors.
- Fog modeled using Koschmieder and Beer-Lambert laws at three visibility densities.
- Achieved 95.1% precision and over 99% recall for vehicle detection within 40m.
- Benchmarks established with TransFusion, BEVFusion, and YOLOv8-m for 3D fusion and 2D restoration.
- Dataset will be open-sourced to promote multi-modal research.
Paper Resources
Source Excerpt
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 rig
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