SAMap: Semantic Alignment for HD Map Detection Domain Generalization Under Varying Weather and Lighting

Wenjie Gao, Haodong Jing, Jiawei Fu, Ziyu Zhu, Shitao Chen, Nanning Zheng

Abstract: High-definition (HD) maps are crucial for autonomous driving systems. Despite recent advances in learning-based HD map prediction methods, these approaches experience significant performance degradation when encountering unseen weather or lighting conditions due to feature distribution discrepancies (domain gaps) of input images. To address this issue, we propose SAMap, a novel map learning framework that enhances domain generalization capabilities of existing models by reducing domain discrepancies in input images. SAMap innovatively introduces a Semantic Aligner, an image-to-image transformation module that aligns images from different domains into a unified domain space while preserving semantic consistency. To train this aligner, we leverage Vision-Language Models (VLMs) that have acquired image-text alignment capabilities. Specifically, we first train a Prompt Learner that combines handcrafted and learnable prompts to capture domain-invariant semantic information. We then train Semantic Aligner through dual supervision mechanisms: a content preservation loss that maintains feature consistency across transformations and a semantic alignment loss that leverages VLM’s encoders to align transformed images with domain-invariant textual representations. Adequate experiments on the NuScenes dataset demonstrate that when integrated with three existing HD map prediction methods, SAMap achieves a performance improvement of up to 11.6% on unseen domains (rain or night conditions), effectively validating its generalization capabilities across domains.

External IDs:dblp:conf/iros/GaoJFZCZ25