Semantic Map Construction Under Complex Weather Scenarios

Semantic maps provide information on road elements, which is crucial for ensuring driving safety. However, previous methods mostly focus on normal weather conditions, neglecting the challenges of scene feature extraction caused by image degradation in complex weather scenarios. To address these challenges, we propose a Normal weather scene-guided Complex weather scene Map Construction Network (NCMC-Net). Firstly, we propose a scene Feature Sharing Module (FSM) based on the complement learning mechanism. This module alternately updates the shared feature pool with features from normal and complex weather scenes during two training stages. Secondly, we design a Feature Refinement Module (FRM) based on the channel attention mechanism to adjust the intensity of multi-channel features selectively. The two modules facilitate the extraction of optimal complex weather scene features guided by features from normal weather scenes. Experimental results on the complex weather scene dataset nuScenes-W validate our model’s performance.