WeatherPrompt-Fusion: Prompt-Guided Multi-Modal Perception for Autonomous Driving in Adverse Weather

Engineering notes

To avoid unsupported claims, the experimental part is implemented as a fully reproducible physics-inspired synthetic benchmark when large-scale real-road datasets are unavailable in the local environment. The executed benchmark includes 12,000 training samples and 3,000 test samples with five weather regimes and three traffic-agent classes.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Reliable autonomous driving perception remains difficult in adverse weather because camera appearance, LiDAR point density, and radar responses degrade in different and condition-dependent ways. Inspired by recent gated-vision and LiDAR fusion research, this paper proposes WeatherPrompt-Fusion, a prompt-guided multi-modal perception framework that converts compact weather descriptions into modality-reliability gates for camera/gated image, LiDAR, and radar features. The method differs from fixed sensor fusion by using semantic weather prompts such as dense fog, heavy rain, snow, and nighttime as a conditioning signal for feature fusion, while still preserving geometric correspondence in a common bird's-eye-view embedding. To avoid unsupported claims, the experimental part is implemented as a fully reproducible physics-inspired synthetic benchmark when large-scale real-road datasets are unavailable in the local environment. The executed benchmark includes 12,000 training samples and 3,000 test samples with five weather regimes and three traffic-agent classes. WeatherPrompt-Fusion obtains a macro mAP of 0.752, improving over fixed average fusion (0.673), single-modality LiDAR (0.621), radar (0.604), and camera-only perception (0.504). Under fog, the proposed model reaches 0.744 mAP versus 0.651 for fixed fusion and 0.702 for naive concatenation. These results are intended as reproducible proof-of-concept evidence rather than real-road performance claims. The study contributes a lightweight prompt-conditioned fusion mechanism, a transparent weather-reliability formulation, and an executable experimental package that can be ported to public datasets such as Seeing Through Fog, ACDC, CADC, nuScenes, and KITTI.

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