In urban pavement crack detection, the single detection platform with visible light sensors is mostly used for detection. Visible light sensors are highly susceptible to lighting conditions. In low-light environments or areas with shadows over the cracks, this may result in false positives or missed detections of pavement cracks. The paper utilizes an image acquisition system that integrates multiple detection platforms, including visible light and infrared sensors, to enhance both the efficiency and accuracy of pavement crack detection. We propose an automatic pavement crack detection approach that uses the similarities and differences between visible light images and infrared image features (MSFSD) to achieve all-weather, real-time detection of pavement cracks. MSFSD mainly includes multi-source image fusion and pixel-level crack detection. In multi-source image fusion, a generative adversarial network (C-GAN) with skip connections and dilated convolutional blocks (CRes2Net blocks) is constructed. The structural similarity index (SSIM) function and the sum of the correlations of differences (SCD) function are used as the loss function of the generator, allowing the fusion image to retain more details and edge information in the source image. In pixel-level crack detection, a crack detection model is constructed, and CRes2Net is used as the core network to obtain pixel-level labels and perform semantic segmentation. It is verified by experiments that MSFSD achieves 85.1% precision and 88.4% recall in different types of crack detection. The MIoU and detection efficiency of MSFSD are 9% and 48% higher than other methods, respectively.

High-speed railways are usually distributed in belts, which are characterized by long spans and uneven geological distribution. When uneven settlement occurs along the high-speed railway, it seriously affects the safe operation of the high-speed railway.^1,2 In the safety of train operation, the settlement time series data along the high-speed railway can provide important data and technical support for mastering the train operation status. To obtain settlement time series data of high-precision and high spatio-temporal resolution along high-speed railways, this paper deeply integrates Global Navigation Satellite System (GNSS) and Interferometric Synthetic-Aperture Radar (InSAR) data.^3,4

This study focuses on developing methodologies to predict the service life of a pavement marking material based on its retroreflectivity. Data from the 2002 National Transportation Product Evaluation Program (NTPEP) pavement marking material test deck in Mississippi are used for model development and model validation. The smoothing spline method and time series modeling are applied to estimate the service lives of different types of materials based on the assumption of a required minimum retroreflectivity value. The same models can also predict the retroreflectivity values at future times for a pavement marking product based on its retroreflectivity values in the past. The validation of the models shows satisfactory accuracy. As demonstrated in a case study, the predicted service lives of the marking materials can be used in life-cycle cost comparisons for selecting pavement marking material.

Pangu is an AI-based model designed for rapid and accurate numerical weather forecasting. To evaluate Pangu’s short- to medium-term weather forecasting skill over various meteorological parameters, this paper validated its performance in predicting temperature, wind speed, wind direction, and barometric pressure using data from over 2000 weather stations in China. Pangu’s performance was compared with ECMWF-HRES and GFS to assess its effectiveness relative to traditional high-precision NWP models under real meteorological conditions. Furthermore, the more recent FuXi and FengWu models were included in the analysis to further validate Pangu’s forecasting skill. The study examined Pangu’s forecast performance from spatial perspectives, evaluated the dispersion of forecast deviations, and analyzed its performance at different lead times and with various initial fields. The iteration precision of Pangu’s four forecast models with lead times of 1 h, 3 h, 6 h, and 24 h was also assessed. Finally, a case study on typhoon track forecasting was conducted to evaluate Pangu’s performance in predicting typhoon paths. The results indicate that Pangu surpasses traditional NWP systems in temperature forecasting, while its performance in predicting wind direction, wind speed and pressure is comparable to them. Additionally, the forecast skill of Pangu diminishes as the lead time extends, but it tends to surpass traditional NWP systems with longer lead times. Moreover, FuXi and FengWu demonstrate even higher accuracy compared to Pangu. Pangu’s performance is also dependent on initial fields, and the temperature forecasting of Pangu is more sensitive to the initial field compared with other meteorological parameters. Furthermore, the iteration precision of Pangu’s 1 h forecast model is significantly lower than that of the other models, but this discrepancy in precision may not be prominently reflected in Pangu’s actual forecasting process due to the greedy algorithm employed. In the case study on typhoon forecasting, Pangu, along with FuXi and FengWu, demonstrates comparable performance in predicting Bebinca’s track compared to ECMWF and outperforms GFS in its track predictions. This study demonstrated Pangu’s applicability in short- to medium-term forecasting of meteorological parameters, showcasing the significant potential of AI-based numerical weather models in enhancing forecast performance.

The robust and high-precision estimation of position and attitude information using a combined global navigation satellite system/inertial navigation system (GNSS/INS) model is essential to a wide range of applications in intelligent driving and smart transportation. GNSS systems are susceptible to inaccuracies and signal interruptions in occluded environments, which lead to unreliable parameter estimations in GNSS/INS based on filter models. To address this issue, in this paper, a GNSS/INS combination model based on factor graph optimization (FGO) is investigated and the robustness of this optimization model is evaluated in comparison to the traditional extended Kalman filter (EKF) model and robust Kalman filter (RKF) model. In this paper, both high- and low-accuracy GNSS/INS combination data are used and the two sets of urban scene data are collected using high- and low-precision consumer-grade inertial guidance systems and an in-vehicle setup. The experimental results demonstrate that the position, velocity, and attitude estimates obtained using the GNSS/INS and the FGO model are superior to those obtained using the traditional EKF and robust EKF methods. In the simulated scenarios involving gross interference and GNSS signal loss, the FGO model achieves optimal results. The maximum improvement rates of the position, velocity, and attitude estimates are 81.1%, 73.8%, and 75.1% compared to the EKF method and 79.8%, 72.1%, and 57.1% compared to the RKF method, respectively.