The Static Fusion Paradox: A Degradation-Centric Survey of Multimodal Perception in Adverse Weather
Abstract
Adverse weather poses a critical challenge to autonomous vehicles due to asymmetric sensor degradation: rain and fog severely degrade cameras, snow attenuates LiDAR by causing severe backscattering, while radar remains robust. This asymmetry breaks conventional fusion architectures that assume consistent sensor reliability—the "static fusion paradox." Existing surveys focus on clear-weather scenarios, leaving gaps in understanding how architectures handle dynamically degrading sensors. This survey provides the first comprehensive analysis of deep learning-based multimodal fusion for asymmetric degradation under adverse weather. We systematically reviewed 30 highly relevant multimodal fusion papers from the broader autonomous vehicle literature (2016–2025), presenting: (1) a degradation-centric fusion taxonomy categorizing architectures by how they handle unreliable sensors, revealing 70% (21/30) use static weighting despite dynamic conditions; (2) a unified restoration-fusion framework analyzing 8 integrated approaches; (3) critical benchmark evaluation of 12 datasets showing only 3 provide severe asymmetric conditions; and (4) a prioritized research roadmap toward weather-agnostic foundation models. Our synthesis establishes that weather-robust perception requires co-evolution of restoration and fusion, not separate optimization, providing both a definitive reference and strategic guidance for next-generation autonomous perception systems.
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Cites
Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602 · see citing works
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897 · see citing works
nuScenes: A Multimodal Dataset for Autonomous Driving
Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., & Beijbom, O. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01164 · Cited by 7,152 · see citing works
PointPillars: Fast Encoders for Object Detection From Point Clouds
Lang, A. H., Vora, S., Caesar, H., Zhou, L., Yang, J., & Beijbom, O. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01298 · Cited by 4,673 · see citing works
Multi-view 3D Object Detection Network for Autonomous Driving
Chen, X., Ma, H., Wan, J., Li, B., & Xia, T. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.691 · Cited by 3,450 · see citing works
End to End Learning for Self-Driving Cars
Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L. D., Monfort, M., Muller, U., Zhang, J., et al. (2016) · arXiv · doi:10.48550/arxiv.1604.07316 · Cited by 3,198 · see citing works
SwinFusion: Cross-domain Long-range Learning for General Image Fusion via Swin Transformer
Ma, J., Tang, L., Fan, F., Huang, J., Mei, X., & Ma, Y. (2022) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2022.105686 · Cited by 1,557 · see citing works
Feng, D., Haase-Schutz, C., Rosenbaum, L., Hertlein, H., Glaser, C., Timm, F., Wiesbeck, W., & Dietmayer, K. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.2972974 · Cited by 1,425 · see citing works
Bevfusion: Multi-task multi-sensor fusion with unified bird’s-eye view representation
Liu, Z., Tang, H., Amini, A., Yang, X., Mao, H., Rus, D. L., & Han, S. (2023) · 2023 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra48891.2023.10160968 · Cited by 1,307 · see citing works
TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers
Bai, X., Hu, Z., Zhu, X., Huang, Q., Chen, Y., Fu, H., & Tai, C.-L. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00116 · Cited by 990 · see citing works
RADIATE: A Radar Dataset for Automotive Perception in Bad Weather
Sheeny, M., Pellegrin, E. D., Mukherjee, S., Ahrabian, A., Wang, S., & Wallace, A. M. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9562089 · Cited by 257 · see citing works
Vision for Looking at Traffic Lights: Issues, Survey, and Perspectives
Jensen, M. B., Philipsen, M. P., Møgelmose, A., et al. (2016) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2015.2509509 · Cited by 252 · see citing works
Real-Time Hybrid Multi-Sensor Fusion Framework for Perception in Autonomous Vehicles
Jahromi, B. S., Tulabandhula, T., & Cetin, S. (2019) · Sensors · doi:10.3390/s19204357 · Cited by 202 · see citing works
Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis
Gao, Y., Piccinini, M., Zhang, Y.-C., Wang, D.-R., Moller, K., Brusnicki, R., Zarrouki, B., Gambi, A., Totz, J. F., Storms, K., et al. (2025) · IEEE Open Journal of Intelligent Transportation Systems · doi:10.1109/ojits.2026.3660686 · Cited by 72 · see citing works
Malawade, A. V., Mortlock, T., & Faruque, M. A. A. (2022) · ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS) · doi:10.1109/iccps54341.2022.00013 · Cited by 63 · see citing works
The Raincouver Scene Parsing Benchmark for Self-Driving in Adverse Weather and at Night
Tung, F., Chen, J., Meng, L., & Little, J. J. (2017) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2017.2723926 · Cited by 45 · see citing works
LiDAR/camera sensor fusion technology for pedestrian detection
Wu, T.-E., Tsai, C., & Guo, J.-I. (2017) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipa.2017.8282301 · Cited by 42 · see citing works
Analyzing Performance of YOLOx for Detecting Vehicles in Bad Weather Conditions
Ashraf, I., Hur, S., Kim, G., & Park, Y. (2024) · Sensors · doi:10.3390/s24020522 · Cited by 26 · see citing works
Autonomous Vehicle Driving in Harsh Weather: Adaptive Fusion Alignment Modeling and Analysis
Hasanujjaman, M., Chowdhury, M. Z., Hossan, M. T., et al. (2023) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-023-08389-1 · Cited by 13 · see citing works
A Gradient Descent Multi-Algorithm Grid Search Optimization of Deep Learning for Sensor Fusion
Booth, T. M., & Ghosh, S. (2023) · IEEE International Systems Conference (SysCon) · doi:10.1109/syscon53073.2023.10131077 · Cited by 6 · see citing works
Çinar, E. M. (2022) · International Conference on Innovations in Intelligent Systems and Applications (INISTA) · doi:10.1109/inista55318.2022.9894141 · Cited by 5 · see citing works
Sharma, T., Chehri, A., Fofana, I., et al. (2024) · IEEE Access · doi:10.1109/access.2024.3486937 · Cited by 4 · see citing works
Unconventional Visual Sensors for Autonomous Vehicles
Li, Y., Moreau, J., & Guzman, J. (2022) · arXiv.org · doi:10.48550/arxiv.2205.09383 · Cited by 4
Flagged first, then by citations
Research works printed but not linked: 21
- Rasmussen, 2021, Laser attenuation in falling snow correlated with measurements of snow particle size distribution, SPIE Defense + Commercial Sensing, 11741
- Bijelic, Seeing through fog: Reconstructing degraded weather conditions using entropy-driven multimodal sensor fusion, 11 682
- Vellaidurai, 2023, Autonomous vehicle detection and tracking based on improved yolov5 and gaussian mixture model particle filter in adverse weather conditions, IEEE Access, 11, 23 249
- Brown, 2021, Hyperspectral technology for autonomous vehicles
- Lin, 2021, A deep learning approach to sensor fusion inference at the edge, IEEE Internet of Things Journal, 9, 1943
- Zhang, 2023, A comparative study of state-of-the-art deep learning algorithms for vehicle detection, Procedia Computer Science, 217, 120
- Yu, 2021, Design and application of an autonomous driving system based on multi-sensor fusion, IEEE Access, 9, 113 214
- Li, 2020, A two-branch neural network for nonhomogeneous dehazing via ensemble learning, IEEE Transactions on Image Processing, 29, 9044
- Yang, 2023, Deepfusionnet: Multitask sensor fusion for 3d object detection and segmentation in autonomous driving, IEEE Robotics and Automation Letters, 8, 1024
- Meyer, Cross-modality knowledge distillation for robust pedestrian detection in low-light and adverse weather conditions, 3130
- Li, 2022, Deep multi-modal learning for vehicle fault detection using transfer and feature fusion, IEEE Access, 10, 31 502
- Chen, Adverseweatherfusion: Deep multimodal sensor fusion for all-weather perception, 642
- Kim, A deep learning technique as a sensor fusion for enhancing positioning in a virtual reality micro-environment, 1
- Dai, 2023, Deep learning-based sensor fusion for robust autonomous driving: A review, MDPI Sensors, 23, 854
- Jahromi, 2019, Real-time hybrid multi-sensor fusion framework for perception in autonomous vehicles, Sensors, 19, 4221
- Zhou, 2023, Deep learning-based adverse weather scene understanding for autonomous vehicles, IEEE Transactions on Intelligent Vehicles, 8, 77
- Li, 2022, Unconventional sensor modalities for autonomous vehicles: Event, polarization, and range-gated imaging
- Guo, 2024, Rrf-net: A dual-stream network for raw and restored image fusion in adverse weather, IEEE Transactions on Intelligent Vehicles, 9, 1251
- 10.1109/iccv.2017.244 · DOI · Paper page
- Guo, 2024, Rrf-net: A dual-stream network for raw and restored image fusion in adverse weather, IEEE Transactions on Intelligent Vehicles, 9, 1251
- Yuan, Uniad: A unified autodriving agent, 20 499
- Authors
- Godugu, S. A.
- Affiliations
- Wayne State University (US)
- Corresponding author
- Godugu, Sri Alekhya
- Venue
- 2026 IEEE Intelligent Vehicles Symposium (IV)
- Publisher
- IEEE
- Published
- 2026-06
- Pages
- 2075-2081
- ISSN
- 1931-0587 (ISSN-L)
- OpenAlex
- W7171828785
- Semantic Scholar
- 9827e392ffa2cc76222faa4a6f3391f0877ffacd
- Cited by
- 0
- References
- 21
- FWCI
- 0.00
- Type
- conference paper
- Open access
- closed
- Licence
- Locations
- IEEE Intelligent Vehicles Symposium (publishedVersion)
- Topics
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Multisensory perception and integration
- Keywords
- multisensory perception, adverse weather, sensor fusion, degradation
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