Real-time roadworks detection and high definition (HD) map updates for autonomous vehicles
File(s)
Published version
Author(s)
Sheng, Shaofan
Formosa, Nicolette
Feng, Yuxiang
Quddus, Mohammed
Type
Journal Article
Abstract
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.
Date Issued
2026-05-01
Date Acceptance
2026-02-23
Citation
Engineering Applications of Artificial Intelligence, 2026, 171
ISSN
0952-1976
Publisher
Elsevier BV
Journal / Book Title
Engineering Applications of Artificial Intelligence
Volume
171
Copyright Statement
© 2026 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Publication Status
Published
Article Number
114321
Date Publish Online
2026-02-26