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Energy efficiency is essential in electric autonomous driving systems, particularly within the framework of smart grid (SG) and vehicle-to-grid (V2G) interactions. However, prior studies have rarely examined energy consumption during lane changes, especially with respect to different driving styles. This study addresses this gap by incorporating driving style variables and employing a quintic polynomial lane-change trajectory model, optimized through Sequential Quadratic Programming (SQP) and the Lagrange multiplier method, to assess energy consumption during lane changes. Additionally, a driving style recognition model categorizes driving behaviors as aggressive, normal, or conservative. The study considers factors such as lane-change duration, speed, and trajectory coefficients to analyze the impact of driving styles on energy usage. Results demonstrate that varying lane-change styles significantly affect energy consumption. These findings offer new perspectives for optimizing the energy efficiency of electric autonomous vehicles within SG environments, supporting the development of adaptive, energy-saving driving strategies and contributing to the advancement of smart grid-connected autonomous electric vehicle systems.

The spatial–temporal sampling errors arising from the differences in geographical locations and measurement times between co-located Global Navigation Satellite System (GNSS) radio occultation (RO) and radiosonde (RS) data represent systematic errors in the three-cornered hat (3CH) method. In this study, we propose a novel spatial–temporal sampling correction method to mitigate the sampling errors associated with both RO–RS and RS–model pairs. We analyze the 3CH processing chain with this new correction method in comparison to traditional approaches, utilizing Fengyun-3E (FY-3E) GNSS Occultation Sounder II (GNOS II) RO data, atmospheric models, and RS datasets from the Hailar and Xisha stations. Overall, the results demonstrate that the improved 3CH method performs better in terms of spatial–temporal sampling errors and the variances of atmospheric parameters, including refractivity, temperature, and specific humidity. Subsequently, we assess the error variances of the FY-3E GNOS II RO, RS and model atmospheric parameters in China, in particular the northern China and southern China regions, based on large ensemble datasets using the improved 3CH data processing chain. The results indicate that the FY-3E GNOS II BeiDou navigation satellite system (BDS) RO and Global Positioning System (GPS) RO show good consistency, with the average error variances of refractivity, temperature, and specific humidity being less than 1.12%2, 0.13%2, and 700%2, respectively. A comparison of the datasets from northern and southern China reveals that the error variances for refractivity are smaller in northern China, while temperature and specific humidity exhibit smaller error variances in southern China, which is attributable to the differing climatic conditions.

Two different Global Navigation Satellite System (GNSS) Reflectometry wind products are to be developed for the Global Navigation Satellite System Occultation Sounder II (GNOS-II) payload on the FengYun-3E (FY-3E) satellite: A global wind product optimized for global low-to-medium wind speeds and a cyclone wind product optimized for high wind speeds in the tropical and extratropical cyclones. This article presents a comprehensive assessment of the GNOS-II global wind product for both global positioning system (GPS) and Beidou (BDS) product at wind speeds below 25 m/s using its data of first six months. Three datasets, including the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis, HY-2B scatterometer and Soil Moisture Active Passive (SMAP) winds, are used for the assessment. The assessment of the Level 1 observables shows that the instrument's performance and calibration are generally stable. For the assessment of retrieved winds, the general performances of GPS and BDS products are similar. The overall wind speed root-mean-square error is 1.54 m/s for GPS-R and 1.44 m/s for BDS-R when compared with ECMWF, 1.48 m/s for GPS-R and 1.36 m/s for BDS-R when compared with HY-2B, and 2.08 m/s for GPS-R and 2.04 m/s for BDS-R when compared with SMAP. The overall wind speed biases for GPS-R and BDS-R are about zeros when compared with each dataset. The assessment is also made by the dependence on time, signal-to-noise ratio, observable, GNSS block, wind speed, incidence angle, and geography. Overall, the FY-3E GNOS-II global wind product is consistent over time and space with an accuracy meeting the requirement of operational use.

The FengYun-3E Global Navigation Satellite System (GNSS) occultation sounder II (FY-3E GNOS II) was launched on 5 July 2021. For the first time, based on the new GNOS II sensor, this mission realizes radio occultation (RO) and reflectometry observations using the navigation signals from the third-generation BeiDou System (BDS-3), and it is hence important to assess and analyze the BDS-3 remote sensing performances relative to other systems. Here, we assessed FY-3E GNOS II RO atmospheric retrievals by inter-comparing with corresponding data from the NCEP FNL global atmospheric analysis and FY-3D GNOS mission. The GNOS RO data quality and consistency of the different FY-3 meteorological satellites, i.e., FY-3D and FY-3E, as well as different GNSS systems (GPS, BDS-2, BDS-3) were analyzed. We find that the FY-3E GNOS II RO data exhibit better quality than FY-3D GNOS, particularly in the number, penetration height toward surface, and global coverage by BDS RO profiles, due to the integration of BDS-2 and BDS-3. Additionally, comparing with co-located NCEP FNL analysis profiles, the mean difference (and standard deviation) of the FY-3E GNOS II RO atmospheric refractivity profile retrievals is found to be smaller than 0.2% (and 1%), in the upper troposphere and lower stratosphere, from 5 to 30 km, and remains consistent at this accuracy and precision level with the FY-3D GNOS RO data. These features provide clear evidence for a high utility of the new GNOS II RO data for weather and climate research and applications.

Semantic segmentation is a critical task in computer vision that aims to assign each pixel in an image a corresponding label on the basis of its semantic content. This task is commonly referred to as dense labeling because it requires pixel-level classification of the image. The research area of semantic segmentation is vast and has achieved critical advances in recent years. Deep learning architectures in particular have shown remarkable performance in generating high-level, hierarchical, and semantic features from images. Among these architectures, convolutional neural networks have been widely used to address semantic segmentation problems. This work aims to review and analyze recent technological developments in image semantic segmentation. It provides an overview of traditional and deep-learning-based approaches and analyzes their structural characteristics, strengths, and limitations. Specifically, it focuses on technical developments in deep-learning-based 2D semantic segmentation methods proposed over the past decade and discusses current challenges in semantic segmentation. The future development direction of semantic segmentation and the potential research areas that need further exploration are also examined.

Fengyun-3C (FY-3C) is the first Chinese satellite that is capable of using the Radio Occultation (RO) technique to retrieve atmospheric profiles. This research evaluates the quality of FY-3C RO profiles including refractivity, temperature, and specific humidity by comparing with corresponding information from the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA-Interim) data over the period of 2015–2018. The evaluation is carried out by calculating and analyzing mean systematic differences between FY-3C and ERA-Interim profiles and corresponding standard deviations over a selected spatial and temporal domain. Results show that the FY-3C RO profiles are overall with good agreements with the ERA-Interim data. Global mean refractivity systematic differences are within ±0.2% from 5 to 30 km altitude range with relative standard deviations of less than 2%. Global temperature mean systematic differences vary within ±0.2 K from a 10- to 20-km altitude range with standard deviations of less than 2 K. Global mean specific humidity differences are found to be within ±0.2 g/kg from 2 to 20 km with standard deviations of less than 1 g/kg. FY-3C profiles show visible latitudinal and altitudinal variations, while the seasonal variations are minor. Sampling errors of refractivity and temperature are also found to be larger at higher latitudinal regions due to RO events being less sampled in the polar region.

With the accumulation of the ionospheric radio occultation (IRO) data observed by Global Navigation Satellite System (GNSS) occultation sounder (GNOS) onboard FengYun-3C (FY3C) satellite, it is possible to use GNOS IRO data for ionospheric climatology research. Therefore, this work aims to validate the feasibility of FY3C/GNOS IRO products in climatology research by comparison with that of Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC), laying the foundation for its application in climatology study. Since previous verification works of FY3C/GNOS were done by comparison with ionosondes, this work matched NmF2/hmF2 of FY3C/GNOS and COSMIC into data pairs to verify the profile-level accuracy of FY3C/GNOS IRO data. The statistical results show that the overall correlation coefficients of both NmF2 and hmF2 are above 0.9, the overall bias and std of NmF2 differences between FY3C/GNOS and COSMIC are −2.19% and 17.48%, respectively, and the bias and std of hmF2 differences are −3.29 and 18.01 km, respectively, indicating a high profile-level precision consistency between FY3C/GNOS and COSMIC. In ionospheric climatology comparison, we divided NmF2/hmF2 of FY3C/GNOS into four seasons, then presented the season median NmF2/hmF2 in 5° × 10° grids and compared them with that of COSMIC. The results show that the ionospheric climatological characteristics of FY3C/GNOS and COSMIC are highly matched, both showing the typical climatological features such as equatorial ionosphere anomaly (EIA), winter anomaly, semiannual anomaly, Weddell Sea anomaly (WSA) and so on, though minor discrepancies do exist like the differences in magnitude of longitude peak structures and WSA, which verifies the reliability of FY3C/GNOS IRO products in ionospheric climatology research.