The technology of autonomous vehicle (AV) is critical in nowadays Intelligent Transportation Systems. To achieve the fully automated operation for AVs, one important prerequisite is the accurate and reliable seamless localization covering complex outdoor-indoor scenarios. Although many solutions have been proposed to support AV localization, it is still challenging inon achieving reliableglobally drift-free positioning in seamless urbanthe unknown outdoor-indoor environments. With the current on-board sensors such as( GNSS, IMU, LiDAR, and cameras, etc.), it is difficult for AVs to achieve accurate drift-free indoor positioning and smooth indoor-outdoor transition due to the lack of GNSS indoorssignals in indoor environments. Meanwhile, challenges remain in reliable navigation under obscured conditions. In this paper, we propose a tightly coupled integration algorithm of GNSS RTK, Ultra-Wide Band (UWB) and Visual Inertial Odometry (VIO) to enhance the accuracy and reliability for AVs seamless localization in challengingharsh environments. The UWB techniquesystem is innovatively incorporated into the AVs navigation system to extendextent the absolute positioning indoorsinto indoor environments. The stereo cameras are utilized to improve positioning continuity and enhance GNSS/UWB usability in outdoor-indoor obscured environments.The visual-inertial-odometry is utilized to enhance the positioning performance in the outdoor-indoor obscured environments. The proposed algorithm is evaluated over a real-world datasetsdataset in complex seamless environments. The results show that the proposed algorithm achieves 0.411m and 0.077m horizontal positioning accuracy in obscured outdoor and indoor environments, yielding 71.2 $\%$ and 18.1 $\%$ improvements compared with the traditional LC integration schemes, respectively. The results show that the incorporation of UWB can instantaneously correct the drifts of VIO in unknown indoor environments. The proposed TC integration algorithm achieves 71.2 $\%$ and 18.1 $\%$ improvements of horizontal positioning in outdoor and indoor tests, compared with the traditional LC integration, respectively.

The geostationary earth orbit (GEO) and inclined geosynchronous orbit (IGSO) satellites of the Beidou navigation satellite system are maneuvered frequently. The broadcast ephemeris can be interrupted for several hours after the maneuver. The orbit-only signal-in-space ranging errors (SISREs) of broadcast ephemerides available after the interruption are over two times larger than the errors during normal periods. To shorten the interruption period and improve the ephemeris accuracy, we propose a two-step orbit recovery strategy based on a piecewise linear thrust model. The turning points of the thrust model are firstly determined by comparison of the kinematic orbit with an integrated orbit free from maneuver; afterward, precise orbit determination (POD) is conducted for the maneuvered satellite by estimating satellite orbital and thrust parameters simultaneously. The observations from the IGS Multi-Global Navigation Satellite System (GNSS) Experiment (MGEX) network and ultra-rapid products of the German Research Center for Geosciences (GFZ) are used for orbit determination of maneuvered satellites from Sep to Nov 2017. The results show that for the rapidly recovered ephemerides, the average orbit-only SISREs are 1.15 and 1.0 m 1 h after maneuvering for GEO and IGSO respectively, which is comparable to the accuracy of Beidou broadcast ephemerides in normal cases.

The negative impacts of class inequality on educational equity in terms of gender, regional development, economic capital, and cultural resources have gradually become a major social problem in China, but so far, relatively little academic attention has been paid to regional educational inequality. Therefore, this paper takes Beijing as a case city and adopts a questionnaire survey form that can reflect the real education gap in the class inequality situation to carry out a study on the generation mechanism and risk hazards of regional education inequality in Beijing. Comprehensive data from government reports, real estate markets, questionnaires, and other sources show that economic development, cultural background, and geographical location are the main causes of regional educational inequality in Beijing.