Autonomous Driving in Adverse Weather: A Multi-Modal Fusion Framework with Uncertainty-Aware Learning for Robust Obstacle Detection

Abstract: Robust obstacle detection in autonomous driving under adverse weather remains a critical challenge due to sensor degradation, visibility reduction, and increased uncertainty. This study proposes an Uncertainty-Aware Multi-Modal Fusion (UAMF) framework that integrates LiDAR, RGB images, and weather priors through a dynamic cross-modal attention mechanism and Bayesian uncertainty modeling. The model adaptively adjusts the fusion weights between sensor modalities according to real-time weather conditions and jointly optimizes detection loss with a KL divergence regularization to quantify predictive uncertainty. Experimental results on the nuScenes, KITTI-Adverse, and CARLA datasets demonstrate that UAMF achieves superior performance across rain, snow, and fog scenarios, with mAP@0.5 reaching 0.78, 0.72, and 0.65, respectively—representing 12–31% gains over existing baselines. Notably, UAMF reduces false positive rates by up to 40% in low-visibility conditions and exhibits a strong correlation (ρ = 0.85) between estimated uncertainty and localization error. Ablation studies confirm the importance of the weather-aware fusion and uncertainty modules, while visibility-level analysis shows improved robustness under <30 m scenarios. The proposed framework offers reliable uncertainty signals for downstream decision-making and is deployable in real-time on embedded platforms. Future work will explore unsupervised weather parameter estimation, uncertainty-aware trajectory forecasting, and cross-domain generalization.

Zhengqing Li and Baljit Singh Bhathal Singh. “Autonomous Driving in Adverse Weather: A Multi-Modal Fusion Framework with Uncertainty-Aware Learning for Robust Obstacle Detection”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.8 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0160808

@article{Li2025,
title = {Autonomous Driving in Adverse Weather: A Multi-Modal Fusion Framework with Uncertainty-Aware Learning for Robust Obstacle Detection},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160808},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160808},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {8},
author = {Zhengqing Li and Baljit Singh Bhathal Singh}
}