Abstract
This paper presents a precipitation-aware sensor ecosystem modelling framework designed to enhance autonomous vehicle (AV) navigation under adverse weather. By integrating spatiotemporal radar-based precipitation nowcasting, deep neural network-based sensor degradation modelling, and adaptive probabilistic sensor fusion, the system dynamically adjusts LiDAR, RADAR, and camera inputs according to precipitation severity and sensor reliability. The proposed framework is evaluated against a fixed-weight baseline detector with uniform multi-sensor fusion under identical training and hardware conditions. On 4.5 TB of multi-modal sensor data collected over 320 driving hours across five precipitation conditions, the framework achieved a 31.2% increase in detection precision, a 27.8% reduction in false positives, and a reduction in mean perception latency from 51 to 43 ms. Radar-based rainfall estimation yielded a mean absolute error of 0.42 mm/h with \(R^{2}=0.91\). Key metrics are reported as mean ± standard deviation over repeated evaluation runs and are further summarized with class-wise Average Precision and statistical variability in the Results section. The dataset used in this study is a custom multi-modal dataset collected during the project and is available from the authors upon reasonable request.
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Data availability
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
References
Kim, J., Park, B.-J. & Kim, J. Empirical analysis of autonomous vehicle’s LiDAR detection performance degradation for actual road driving in rain and fog. Sensors 23(6), 2972 (2023).
Yeong, D. J., Velasco-Hernandez, G., Barry, J. & Walsh, J. Sensor and sensor fusion technology in autonomous vehicles: A review. Sensors 21(6), 2140 (2021).
Matos, F., Bernardino, J., Duraes, J. & Cunha, J. A survey on sensor failures in autonomous vehicles: Challenges and solutions. Sensors 24(16), 5108 (2024).
Zhang, Y., Carballo, A., Yang, H. & Takeda, K. Perception and sensing for autonomous vehicles under adverse weather conditions: A survey. ISPRS J. Photogramm. Remote. Sens. 196, 146–177 (2023).
Qian, H., Wang, M., Zhu, M. & Wang, H. A review of multi-sensor fusion in autonomous driving. Sensors 25(19), 6033 (2025).
Bayramov, E. & Istenes, Z. Weather-informed vision enhancement for autonomous vehicles in adverse conditions. Pollack Periodica. (2025).
Sezgin, F., Vriesman, D., Steinhauser, D., Lugner, R. & Brandmeier, T. Safe autonomous driving in adverse weather: Sensor evaluation and performance monitoring. In Proceedings of the IEEE Intelligent Vehicles Symposium (IV), 1–6 (2023).
Diaz-Ruiz, C. A. et al. Ithaca365: Dataset and driving perception under repeated and challenging weather conditions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 21383–21392 (2022).
Alzanin, S. Explainable artificial intelligence with temporal convolutional networks for adverse weather condition detection in driverless vehicles. Sci. Rep. 15(1), 19475 (2025).
Shi, X. et al. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. In Advances in Neural Information Processing Systems (NeurIPS) (2015).
Choi, J., Kim, Y., Kim, K.-H., Jung, S.-H., & Cho, I. PCT-CycleGAN: Paired complementary temporal cycle-consistent adversarial networks for radar-based precipitation nowcasting. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 348–358 (2023).
Rahimi, R. et al. Global precipitation nowcasting of integrated multi-satellite retrievals for GPM: A U-Net convolutional LSTM architecture. J. Hydrometeorol. 25(6), 947–963 (2024).
Yin, T., Zhou, X., & Krahenbuhl, P. Center-based 3D object detection and tracking. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 11784–11793 (2021).
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Open access funding provided by Symbiosis International (Deemed University).
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S.K. and R.B. were involved in the conception of the study, methodology, data analysis, writing the original draft and revision of the manuscript preparation. R.B., N.S.B., and M.S. were involved in the supervision and revision of manuscript preparation. Finally, all the authors reviewed and approved the manuscript.
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Kalra, S., Beniwal, R., Beniwal, N.S. et al. Precipitation-aware sensor ecosystem modelling for performance-driven autonomous vehicle navigation. Sci Rep (2026). https://doi.org/10.1038/s41598-026-44435-2
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DOI: https://doi.org/10.1038/s41598-026-44435-2
