Detection in adverse weather conditions for autonomous vehicles via deep learning
Open accessAbstract
Weather detection systems (WDS) have an indispensable role in supporting the decisions of autonomous vehicles, especially in severe and adverse circumstances. With deep learning techniques, autonomous vehicles can effectively identify outdoor weather conditions and thus make appropriate decisions to easily adapt to new conditions and environments. This paper proposes a deep learning (DL)-based detection framework to categorize weather conditions for autonomous vehicles in adverse or normal situations. The proposed framework leverages the power of transfer learning techniques along with the powerful Nvidia GPU to characterize the performance of three deep convolutional neural networks (CNNs): SqueezeNet, ResNet-50, and EfficientNet. The developed models have been evaluated on two up-to-date weather imaging datasets, namely, DAWN2020 and MCWRD2018. The combined dataset has been used to provide six weather classes: cloudy, rainy, snowy, sandy, shine, and sunrise. Experimentally, all models demonstrated superior classification capacity, with the best experimental performance metrics recorded for the weather-detection-based ResNet-50 CNN model scoring 98.48%, 98.51%, and 98.41% for detection accuracy, precision, and sensitivity. In addition to this, a short detection time has been noted for the weather-detection-based ResNet-50 CNN model, involving an average of 5 (ms) for the time-per-inference step using the GPU component. Finally, comparison with other related state-of-art models showed the superiority of our model which improved the classification accuracy for the six weather conditions classifiers by a factor of 0.5–21%. Consequently, the proposed framework can be effectively implemented in real-time environments to provide decisions on demand for autonomous vehicles with quick, precise detection capacity.
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DL's neural network-based approach is particularly adept at handling complex models that traditional techniques cannot.
“DL's neural network-based approach is particularly adept at handling complex models that traditional techniques cannot [12].”
From Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches (2024) · cites this paper as [12]
Study [12] evaluated SqueezeNet, ResNet-50, and EfficientNet-b0 on a combined MCWDS2018 and DAWN2020 dataset for six weather categories; ResNet-50, EfficientNet-b0, and SqueezeNet achieved 98.48%, 97.78%, and 96.05% accuracy, respectively.
“The ResNet-50, EfficientNet-b0, and SqueezeNet models achieved 98.48%, 97.78%, and 96.05% accuracy, respectively, in the experiments, demonstrating remarkable accuracy rates while preserving quick inference times using GPU acceleration.”
From Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches (2024) · cites this paper as [12]
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Evaluating the Performance of YOLOP for Lane Detection with Challenging Road Conditions
Shehada, D. J. M., & Bouridane, A. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-5035-1_5
Automatic Traffic Surveillance Using Deep Learning Model Under Extreme Climatic Conditions
Venkataramani, S. G. (2025) · International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT) · doi:10.1109/idciot64235.2025.10915125
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Vehicle and Pedestrian Detection in Extreme Weather Based on Improved Rt-Detr
Liu, M., Zhang, R., Gao, C., & Zhang, S. (2025) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc65474.2025.11090313
Lightweight Model for Weather Prediction
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Domain-Adaptive Reinforcement Learning Framework for Robust Autonomous Vehicle Navigation
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AWDAN: adverse-weather data augmentation network for enhanced traffic object detection
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Multi-Modal Feature Fusion Distance Gating 3D Imaging Based on Edge Computing
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Challenges, Advances and Future Prospects of Vehicle Detection in Snow
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ED-Former: Efficient dehazing transformer with Attention-Adaptive Feed-Forward Network
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Weather Classification with Convolutional Neural Network for Autonomous Driving
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- Authors
- Al-Haija, Q. A., Gharaibeh, M., & Odeh, A.
- Affiliations
- Department of Computer Science/Cybersecurity, Princess Sumaya University for Technology, Amman 11941, Jordan
- Princess Sumaya University for Technology (JO)
- Venue
- AI
- Publisher
- MDPI AG
- Published
- 2022-04-18
- Volume
- 3
- Issue
- 2
- Pages
- 303-317
- ISSN
- 2673-2688 (ISSN-L)
- OpenAlex
- W4224282157
- Cited by
- 81
- References
- 39
- Citations by year
- 2026: 11 · 2025: 25 · 2024: 25 · 2023: 13 · 2022: 7
- FWCI
- 5.73
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- AI (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- autonomous vehicles, adverse weather, image classification, transfer learning, ResNet50, SqueezeNet, EfficientNet, real-time detection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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