Search Results (3)

GPSs typically have an accuracy ranging from a few meters to several tens of meters. However, when corrected using various methods, they can achieve an accuracy of several tens of centimeters. In autonomous driving, a positioning accuracy of less than 50 cm is required for lane-level positioning, route generation, and navigation. However, in environments where GPS signals are blocked, such as tunnels and underground roads, absolute positioning is impossible. Instead, relative positioning methods integrating IMU, IVN, and cameras are used. These methods are influenced by numerous variables, however, such as vehicle speed and road conditions, resulting in lower accuracy. In this study, we conducted experiments on current vehicle navigation technologies using an autonomous driving simulation vehicle in the Suri–Suam Tunnel of the Seoul Metropolitan Area 1st Ring Expressway. To recognize objects (lane markings/2D/3D) for position correction inside the tunnel, data on tunnel and underground road infrastructure in Seoul and Gyeonggi Province was collected, processed, refined, and trained. Additionally, a Loosely Coupled-based Kalman Filter was designed and applied for the fusion of GPSs, IMUs, and IVNs. As a result, an error of 113.62 cm was observed in certain sections. This suggests that while the technology is applicable for general vehicle lane-level navigation in ultra-long tunnels spanning several kilometers for public service, it falls short of meeting the precision required for autonomous driving systems, which demand lane-level accuracy. Therefore, it was concluded that infrastructure-based absolute positioning technology is necessary to enable precise navigation inside tunnels. Full article

The settlement and deformation of abandoned mining tunnels can lead to cracking, deformation, or even the collapse of surface structures. Recently, a dual-direction, four-lane expressway, designed a speed of 100 km/h, is planned to be constructed between Yuanling County and Chenxi County. This expressway will pass through a long-abandoned refractory clay mining area in Chenxi County. This study focuses on this abandoned mining area and employs the Electrical Resistivity Tomography (ERT) method to investigate the underground conditions, aiming to determine the location and scale of the subterranean goaf. A total of five survey lines were deployed for the investigation. The inversion results indicate the presence of five low-resistivity anomalies in the underground structure (with six low-resistivity anomalies identified along line L1). These low-resistivity anomalies are preliminarily interpreted as subsurface cavities. Subsequent borehole verification revealed that the five low-resistivity anomalies correspond to a total of eight water-filled cavities, including six abandoned mining tunnels and two karst caves. At the location K33+260~K33+350, a large low-resistivity anomaly was identified which actually consisted of three closely spaced water-filled abandoned mining tunnels. Additionally, the surrounding strata primarily consisted of fractured mudstone, which has a high water content and thus exhibits low resistivity. These two factors combined resulted in the three water-filled abandoned mining tunnels appearing as a single large low-resistivity anomaly in the inversion profile. Meanwhile, at K33+50~K33+110, two water-filled abandoned mining tunnels were found. These tunnels are far apart along line L1 but are relatively close to each other on the other four survey lines. Consequently, in the inversion results, line L1 displays these as two separate low-resistivity anomalies, while the other four survey lines show them as a single large low-resistivity anomaly. Based on the 2D inversion results, a 3D model of the study area was constructed. This model provides a more intuitive visualization of the underground cavity structures in the study area. The findings not only serve as a reference for the subsequent remediation of the goaf area but also offer new insights into the detection of abandoned mining tunnels. Full article

The resurrection of landslides often occurs in complex strata, where there are often multiple water-resisting zones and weak interlayers. The groundwater level has a significant influence on landslide stability and can lead to the formation of composite landslides and increase the probability of landslide resurrection. In a field investigation of the K39 +400 composite landslide of Wenma Expressway, the deformation characteristics of the landslide surface were obtained by analyzing 34 cracks on the landslide surface. The deep deformation characteristics of the landslide were analyzed by studying the deformation data obtained from deep borehole monitoring. The deformation zone characteristics of landslides were assessed by integrating surface and deep deformation data in the landslide area. The resurrections of shallow landslides in Area I and deep landslides in Area III were due mainly to the long-term high underground water level in the landslide. The stability of the landslide was calculated under various depths of drainage tunnel, and the results showed that the slope was in critical condition when the depth of the drainage tunnel was 15 m; the slope was basically stable when the depth of the drainage tunnel was 20 m and 25 m. When the depth of the drainage tunnel reached more than 30 m, the slope was in a stable state. Groundwater level was found to be the main factor affecting landslide deformation. This case study shows the importance of zoning the deformation characteristics of composite landslides, and the important influence of the groundwater level on landslide resurrection. Full article