Lane information plays an important role in high-definition (HD) maps because it provides invaluable road information for autonomous vehicles. The most widely used lane extraction method for HD maps is based on mobile mapping systems, and it is prohibitively costly and time-consuming. In this study, we propose a novel approach for lane information extraction based on crowdsourcing vehicles equipped with monocular cameras and global navigation satellite system devices for recording road images and position data. First, we propose a lane mask propagation network to detect the lane markings in images, which are then projected from a perspective space into a three-dimensional space in accordance with the position data. Second, we propose a data management method to store lane information in the cloud data center. An improved density-based spatial clustering of applications with noise clustering algorithm and a gradual fitting algorithm are used to remove the outliers and improve the lane data accuracy. The proposed method is quantitatively evaluated against a real-world HD map produced by a mobile mapping vehicle. The experimental results show that more than 80% of the extracted lane markings meet the accuracy requirements of HD maps. In conclusion, our method can be used as a low-cost and efficient approach for updating the lane information in HD maps for autonomous vehicles.

Vision-based lane-detection methods provide low-cost density information about roads for autonomous vehicles. In this paper, we propose a robust and efficient method to expand the application of these methods to cover low-speed environments. First, the reliable region near the vehicle is initialized and a series of rectangular detection regions are dynamically constructed along the road. Then, an improved symmetrical local threshold edge extraction is introduced to extract the edge points of the lane markings based on accurate marking width limitations. In order to meet real-time requirements, a novel Bresenham line voting space is proposed to improve the process of line segment detection. Combined with straight lines, polylines, and curves, the proposed geometric fitting method has the ability to adapt to various road shapes. Finally, different status vectors and Kalman filter transfer matrices are used to track the key points of the linear and nonlinear parts of the lane. The proposed method was tested on a public database and our autonomous platform. The experimental results show that the method is robust and efficient and can meet the real-time requirements of autonomous vehicles.

High-definition (HD) maps serve as crucial infrastructure for autonomous driving technology, facilitating vehicles in positioning, environmental perception, and motion planning without being affected by weather changes or sensor-visibility limitations. Maintaining precision and freshness in HD maps is paramount, as delayed or inaccurate information can significantly impact the safety of autonomous vehicles. Utilizing crowdsourced data for HD map updating is widely recognized as a superior method for preserving map accuracy and freshness. Although it has garnered considerable attention from researchers, there remains a lack of comprehensive exploration into the entire process of updating HD maps through crowdsourcing. For this reason, it is imperative to review and discuss crowdsourcing techniques. This paper aims to provide an overview of the overall process of crowdsourced updates, followed by a detailed examination and comparison of existing methodologies concerning the key techniques of data collection, information extraction, and change detection. Finally, this paper addresses the challenges encountered in crowdsourced updates for HD maps.

Vehicles have been increasingly equipped with GPS receivers to record their trajectories, which we call floating car data. Compared with other data sources, these data are characterized by low cost, wide coverage, and rapid updating. The data have become an important source for road network extraction. In this paper, we propose a novel approach for mining road networks from floating car data. First, a Gaussian model is used to transform the data into bitmap, and the Otsu algorithm is utilized to detect road intersections. Then, a clothoid-based method is used to resample the GPS points to improve the clustering accuracy, and the data are clustered based on a distance-direction algorithm. Last, road centerlines are extracted with a weighted least squares algorithm. We report on experiments that were conducted on floating car data from Wuhan, China. To conclude, existing methods are compared with our method to prove that the proposed method is practical and effective.

As the frontier of urbanization, urban-rural fringes (URFs) transitionally connect urban construction regions to the rural hinterland, and its identification is significant for the study of urbanization-related socioeconomic changes and human dynamics. Previous research on URF identification has predominantly relied on remote sensing data, which often provides a uniform overhead perspective with limited spatial resolution. As an additional data source, Street View Images (SVIs) offer a valuable human-related perspective, efficiently capturing intricate transitions from urban to rural areas. However, the abundant visual information offered by SVIs has often been overlooked and multi-modal techniques have seldom been explored to integrate multi-source data for delineating URFs. To address this gap, this study proposes a Transformed-based multi-modal methodology for identifying URFs, which includes a street view panorama classifier and a remote sensing classification model. In the study area of Beijing, the experimental results indicate that an URF with a total area of 731.24 km ² surrounds urban cores, primarily located between the fourth and sixth ring roads. The effectiveness of the proposed method is demonstrated through comparative experiments with traditional URF identification methods. Additionally, a series of ablation studies demonstrate the efficacy of incorporating multi-source data. Based on the delineated URFs in Beijing, this research introduced Points of Interest (POI) data and commuting data to analyze the socioeconomic characteristics of URFs. The findings indicate that URFs are characterized by longer commuting distances and less diverse restaurant consumption patterns compared to more urbanized regions. This study enables the accurate identification of URFs through the Transform-based multi-modal approach integrating street view images. Furthermore, it provides a humancentric comprehension of URFs, which is essential for informing strategies of urban planning and development.