Autonomous vehicles face major perception and navigation challenges in adverse weather such as rain, fog, and snow, which degrade the performance of LiDAR, RADAR, and RGB camera sensors. While each sensor type offers unique strengths, such as RADAR robustness in poor visibility and LiDAR precision in clear conditions, they also suffer distinct limitations when exposed to environmental obstructions. This study proposes LRC-WeatherNet, a novel multi-sensor fusion framework that integrates LiDAR, RADAR, and camera data for real-time classification of weather conditions. By employing both early fusion using a unified Bird's Eye View representation and mid-level gated fusion of modality-specific feature maps, our approach adapts to the varying reliability of each sensor under changing weather. Evaluated on the extensive MSU-4S dataset covering nine weather types, LRC-WeatherNet achieves superior classification performance and computational efficiency, significantly outperforming unimodal baselines in adverse conditions. This work is the first to combine all three modalities for robust, real-time weather classification in autonomous driving. We release our trained models and source code in https://github.com/nouralhudaalbashir/LRC-WeatherNet.

翻译：自动驾驶车辆在雨、雾、雪等恶劣天气下面临严重的感知与导航挑战，这些天气条件会降低激光雷达、毫米波雷达和RGB相机传感器的性能。尽管每种传感器具有独特优势——例如毫米波雷达在低能见度下的鲁棒性以及激光雷达在晴朗天气中的精度——它们在面对环境障碍时仍存在明显局限。本研究提出LRC-WeatherNet，一种融合激光雷达、毫米波雷达与相机数据的新型多传感器融合框架，用于天气状况的实时分类。通过采用基于统一鸟瞰视图表示的早期融合及模态特异性特征图的中间层门控融合，该方法能自适应各传感器在不同天气下的可靠性变化。在涵盖九种天气类型的大规模MSU-4S数据集上评估表明，LRC-WeatherNet实现了卓越的分类性能与计算效率，在恶劣条件下显著优于单模态基线方法。本研究首次融合三种模态以实现自动驾驶中鲁棒的实时天气分类。我们已在https://github.com/nouralhudaalbashir/LRC-WeatherNet 开源训练模型与源代码。