Efficiently updating real-time road maps is essential for autonomous driving and significantly influences human driving decisions. Existing high-definition (HD) maps are often based on static data and neglect real-time road information, particularly temporary traffic control details related to construction. In this study, we propose a method to identify and update lane-level information for roads in restricted areas using only a digital video recorder and a low-cost global navigation satellite system (GNSS). This research proposes a Road Temporary Traffic Control Network (RTTCNet) to detect control devices and lane markings and to optimize a 3D reconstruction method that can accurately position control devices on HD maps. The method identifies areas with restricted road access and analyzes the spatial relationship between control devices and lane lines to update the HD map. Our method effectively addresses the high costs and inefficiencies associated with conventional map-updating methods. Experimental tests on various road types in Shanghai demonstrated 94.85% accuracy in identifying restricted areas for road access from single images, with a mean positioning error of 3.86 meters for temporary traffic control devices on HD maps. This method enables real-time lane-level updates for road information on HD maps and more effective decision-making support for autonomous driving.

As the service life of metro tunnels increases, the lining and associated components often sustain damage from various factors, jeopardizing operational safety and structural integrity. Traditional manual inspections and digital image processing methods fall short owing to the challenging internal environment, suboptimal lighting conditions, and the highly similar textures shared by components and tunnel linings. To address these challenges, we focus on linear array images that capture the tunnel’s inner wall and propose FESS-SAM for the semantic segmentation of all elements without the need for additional training. We introduce an innovative iterative segmentation mechanism utilizing hierarchical stacking thresholds to extract instance-level masks. It alleviates issues posed by mask redundancy, complex features, and the inherent limitations of the Segment Anything Model (SAM). Furthermore, with the establishment of the tunnel component ontology, we address the pain point of semantic deficiency by employing a random forest classifier trained on high-dimensional image features. The experimental results obtained from a dataset of 996 images show a mean Intersection over Union (mIoU) of 0.819 and an F1 score of 0.895 across six representative tunnel elements. These results indicate that FESS-SAM outperforms existing supervised segmentation models in terms of visualization and accuracy metrics. This advancement addresses the data dependency issues in complex scenarios and achieves millimeter-level precision in segmenting critical tunnel components. By innovatively introducing a cutting-edge vision foundation model into real-world infrastructure management, it offers a robust and scalable solution for large-scale, automated, and precise metro tunnel maintenance.

Different types of roads in complex road networks may run side-by-side or across in 2D or 3D spaces, which causes mismatched segments using existing online map-matching algorithms. A driving scenario that represents the driving environment can inform map-matching algorithms. Images from vehicle cameras contain extensive information about driving scenarios, such as surrounding key objects. This research utilized vehicle images and developed an object-based method to classify driving scenarios (Object-Based Driving-Scenario Classification: OBDSC) to calculate the probabilities of the current image in predefined types of driving scenarios. We implemented an online map-matching algorithm with the OBDSC method (OMM-OBDSC) to obtain optimal matching segments. The algorithm was tested on nine trajectories and OpenStreetMap data in Shanghai and compared with five benchmark algorithms in terms of the match rate, recall and accuracy. The OBDSC method is also applied to the benchmark algorithms to verify the effectiveness of map matching. The results show that our algorithm outperforms the benchmark algorithms with both the original interval and downsampled intervals (96.6%, 96.5%, 93.7% on average with 1–20 s intervals for the three metrics, respectively). The average match rate has improved by 8.9% for all benchmark algorithms after the addition of the OBDSC method.