The increasing prevalence of roadworks poses significant challenges to maintaining accurate and up-to-date high-definition (HD) maps, crucial for autonomous vehicle (AV) safety and efficiency. Current methods for updating HD maps are expensive, time-consuming, and not responsive to real-time changes. This paper proposes a real-time, low-cost pipeline for updating HD maps with roadworks information using a monocular camera and a Contrastive Language–Image Pre-Training-based Vision Language Model (VLM), achieving robust few-shot sign recognition with minimal annotated data. The workflow is designed for low-cost, real-time deployment using only monocular camera input, and supports rapid, incremental HD map updates directly in OpenDRIVE format. Extensive experiments demonstrate that our system outperforms conventional baselines (e.g., fine-tuned You Only Look Once (YOLO) v11) not only in data-scarce settings but also across challenging environmental conditions. The recognition model is trained on a diverse dataset of 3752 real and virtual images, enhanced through data augmentation techniques. The model achieves a 97.12% recognition rate on a test dataset of 752 images and a root mean square error (RMSE) of less than 1.2 m for positional accuracy, processing single image inputs in 1.54 s. By leveraging the OpenDRIVE format, this approach ensures seamless data exchange between different HD map systems, facilitating real-time updates that accurately reflect current road conditions. The methodology demonstrates significant benefits in terms of responsiveness, cost and time efficiency, enhanced safety, and flexibility. Trials on the United Kingdom motorways validate the pipeline's effectiveness, offering a robust solution to dynamic road conditions and enabling safer, more efficient AV navigation.
Sheng et al. (2026) studied this question.