# Weather and Light Level Classification for Autonomous Driving: Dataset, Baseline and Active Learning arXiv: https://arxiv.org/abs/2104.14042 · HTML: https://mlsanity.com/paper/2104.14042/ ## Key facts - Cited by 20 papers in the map (data through 14 Oct 2026). - Builds on 13 of its 27 references that are in the map. - First posted on arXiv 28 Apr 2021. ## Builds on (most cited first, 13 of 13) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Training Region-based Object Detectors with Online Hard Example Mining](https://mlsanity.com/paper/1604.03540/) (2016) - [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018) - [Learning Loss for Active Learning](https://mlsanity.com/paper/1905.03677/) (2019) - [Cost-Effective Active Learning for Deep Image Classification](https://mlsanity.com/paper/1701.03551/) (2017) - [A Survey on Data Collection for Machine Learning: a Big Data -- AI Integration Perspective](https://mlsanity.com/paper/1811.03402/) (2018) - [OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving](https://mlsanity.com/paper/2102.07448/) (2021) - [MultiNet++: Multi-Stream Feature Aggregation and Geometric Loss Strategy for Multi-Task Learning](https://mlsanity.com/paper/1904.08492/) (2019) - [Challenges in Designing Datasets and Validation for Autonomous Driving](https://mlsanity.com/paper/1901.09270/) (2019) - [SoilingNet: Soiling Detection on Automotive Surround-View Cameras](https://mlsanity.com/paper/1905.01492/) (2019) - [A CNN-RNN Architecture for Multi-Label Weather Recognition](https://mlsanity.com/paper/1904.10709/) (2019) - [WeatherNet: Recognising weather and visual conditions from street-level images using deep residual learning](https://mlsanity.com/paper/1910.09910/) (2019) - [Weather Classification: A new multi-class dataset, data augmentation approach and comprehensive evaluations of Convolutional Neural Networks](https://mlsanity.com/paper/1808.00588/) (2018) ## Built on by (most cited first, 20 of 20) - [Description of Corner Cases in Automated Driving: Goals and Challenges](https://mlsanity.com/paper/2109.09607/) (2021) - [SynWoodScape: Synthetic Surround-view Fisheye Camera Dataset for Autonomous Driving](https://mlsanity.com/paper/2203.05056/) (2022) - [Spatio-Contextual Deep Network Based Multimodal Pedestrian Detection For Autonomous Driving](https://mlsanity.com/paper/2105.12713/) (2021) - [LiMoSeg: Real-time Bird's Eye View based LiDAR Motion Segmentation](https://mlsanity.com/paper/2111.04875/) (2021) - [Adversarial Attacks on Multi-task Visual Perception for Autonomous Driving](https://mlsanity.com/paper/2107.07449/) (2021) - [Active Source Free Domain Adaptation](https://mlsanity.com/paper/2205.10711/) (2022) - [An Online Learning System for Wireless Charging Alignment using Surround-view Fisheye Cameras](https://mlsanity.com/paper/2105.12763/) (2021) - [Optical Flow for Autonomous Driving: Applications, Challenges and Improvements](https://mlsanity.com/paper/2301.04422/) (2023) - [Deep Kernel Methods Learn Better: From Cards to Process Optimization](https://mlsanity.com/paper/2303.14554/) (2023) - [Woodscape Fisheye Object Detection for Autonomous Driving -- CVPR 2022 OmniCV Workshop Challenge](https://mlsanity.com/paper/2206.12912/) (2022) - [Surround-View Cameras based Holistic Visual Perception for Automated Driving](https://mlsanity.com/paper/2206.05542/) (2022) - [LiDAR-BEVMTN: Real-Time LiDAR Bird's-Eye View Multi-Task Perception Network for Autonomous Driving](https://mlsanity.com/paper/2307.08850/) (2023) - [Multi-camera Bird's Eye View Perception for Autonomous Driving](https://mlsanity.com/paper/2309.09080/) (2023) - [DaF-BEVSeg: Distortion-aware Fisheye Camera based Bird's Eye View Segmentation with Occlusion Reasoning](https://mlsanity.com/paper/2404.06352/) (2024) - [SpikiLi: A Spiking Simulation of LiDAR based Real-time Object Detection for Autonomous Driving](https://mlsanity.com/paper/2206.02876/) (2022) - [VALISENS: A Validated Innovative Multi-Sensor System for Cooperative Automated Driving](https://mlsanity.com/paper/2505.06980/) (2025) - [WoodScape Motion Segmentation for Autonomous Driving -- CVPR 2023 OmniCV Workshop Challenge](https://mlsanity.com/paper/2401.00910/) (2023) - [Neural Rendering based Urban Scene Reconstruction for Autonomous Driving](https://mlsanity.com/paper/2402.06826/) (2024) - [Impact of Video Compression Artifacts on Fisheye Camera Visual Perception Tasks](https://mlsanity.com/paper/2403.16338/) (2024) - [Heuristic Style Transfer for Real-Time, Efficient Weather Attribute Detection](https://mlsanity.com/paper/2604.13947/) (2026) ## Abstract (arXiv) Autonomous driving is rapidly advancing, and Level 2 functions are becoming a standard feature. One of the foremost outstanding hurdles is to obtain robust visual perception in harsh weather and low light conditions where accuracy degradation is severe. It is critical to have a weather classification model to decrease visual perception confidence during these scenarios. Thus, we have built a new dataset for weather (fog, rain, and snow) classification and light level (bright, moderate, and low) classification. Furthermore, we provide street type (asphalt, grass, and cobblestone) classification, leading to 9 labels. Each image has three labels corresponding to weather, light level, and street type. We recorded the data utilizing an industrial front camera of RCCC (red/clear) format with a resolution of 1024×1084. We collected 15k video sequences and sampled 60k images. We implement an active learning framework to reduce the dataset's redundancy and find the optimal set of frames for training a model. We distilled the 60k images further to 1.1k images, which will be shared publicly after privacy anonymization. There is no public dataset for weather and light level classification focused on autonomous driving to the best of our knowledge. The baseline ResNet18 network used for weather classification achieves state-of-the-art results in two non-automotive weather classification public datasets but significantly lower accuracy on our proposed dataset, demonstrating it is not saturated and needs further research.