다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Обнаружение транспортных средств на изображениях загородных шоссе на основе метода Single shot multibox Detector

In the last decades, 3D city models appear to have been predominantly used for visualisation; however, today they are being increasingly employed in a number of domains and for a large range of tasks beyond visualisation. In this paper, we seek to understand and document the state of the art regarding the utilisation of 3D city models across multiple domains based on a comprehensive literature study including hundreds of research papers, technical reports and online resources. A challenge in a study such as ours is that the ways in which 3D city models are used cannot be readily listed due to fuzziness, terminological ambiguity, unclear added-value of 3D geoinformation in some instances, and absence of technical information. To address this challenge, we delineate a hierarchical terminology (spatial operations, use cases, applications), and develop a theoretical reasoning to segment and categorise the diverse uses of 3D city models. Following this framework, we provide a list of identified use cases of 3D city models (with a description of each), and their applications. Our study demonstrates that 3D city models are employed in at least 29 use cases that are a part of more than 100 applications. The classified inventory could be useful for scientists as well as stakeholders in the geospatial industry, such as companies and national mapping agencies, as it may serve as a reference document to better position their operations, design product portfolios, and to better understand the market.

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Applications of 3D City Models: State of the Art Review

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand, and anticipate human behavior becomes increasingly important. Spec...

Human motion trajectory prediction: a survey:

Artificial potential fields and optimal controllers are two common methods for path planning of autonomous vehicles. An artificial potential field method is capable of assigning different potential functions to different types of obstacles and road structures and plans the path based on these potential functions. It does not, however, include the vehicle dynamics in the path-planning process. On the other hand, an optimal path-planning controller integrated with vehicle dynamics plans an optimal feasible path that guarantees vehicle stability in following the path. In this method, the obstacles and road boundaries are usually included in the optimal control problem as constraints and not with any arbitrary function. A model predictive path-planning controller is introduced in this paper such that its objective includes potential functions along with the vehicle dynamics terms. Therefore, the path-planning system is capable of treating different obstacles and road structures distinctly while planning the optimal path utilizing vehicle dynamics. The path-planning controller is modeled and simulated on a CarSim vehicle model for some complicated test scenarios. The results show that, with this path-planning controller, the vehicle avoids the obstacles and observes road regulations with appropriate vehicle dynamics. Moreover, since the obstacles and road regulations can be defined with different functions, the path-planning system plans paths corresponding to their importance and priorities.

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A Potential Field-Based Model Predictive Path-Planning Controller for Autonomous Road Vehicles

The current low-cost global navigation satellite systems (GNSS) receiver cannot calculate satisfactory positioning results for pedestrian applications in urban areas with dense buildings due to multipath and non-line-of-sight effects. We develop a rectified positioning method using a basic three-dimensional city building model and ray-tracing simulation to mitigate the signal reflection effects. This proposed method is achieved by implementing a particle filter to distribute possible position candidates. The likelihood of each candidate is evaluated based on the similarity between the pseudorange measurement and simulated pseudorange of the candidate. Finally, the expectation of all the candidates is the rectified positioning of the proposed map method. The proposed method will serve as one sensor of an integrated system in the future. For this purpose, we successfully define a positioning accuracy based on the distribution of the candidates and their pseudorange similarity. The real data are recorded at an urban canyon environment in the Chiyoda district of Tokyo using a commercial grade u-blox GNSS receiver. Both static and dynamic tests were performed. With the aid of GLONASS and QZSS, it is shown that the proposed method can achieve a 4.4-m 1ź positioning error in the tested urban canyon area.

3D building model-based pedestrian positioning method using GPS/GLONASS/QZSS and its reliability calculation

The accuracy of the positions of a pedestrian is very important and useful information for the statistics, advertisement, and safety of different applications. Although the GPS chip in a smartphone is currently the most convenient device to obtain the positions, it still suffers from the effect of multipath and nonline-of-sight propagation in urban canyons. These reflections could greatly degrade the performance of a GPS receiver. This paper describes an approach to estimate a pedestrian position by the aid of a 3-D map and a ray-tracing method. The proposed approach first distributes the numbers of position candidates around a reference position. The weighting of the position candidates is evaluated based on the similarity between the simulated pseudorange and the observed pseudorange. Simulated pseudoranges are calculated using a ray-tracing simulation and a 3-D map. Finally, the proposed method was verified through field experiments in an urban canyon in Tokyo. According to the results, the proposed approach successfully estimates the reflection and direct paths so that the estimate appears very close to the ground truth, whereas the result of a commercial GPS receiver is far from the ground truth. The results show that the proposed method has a smaller error distance than the conventional method.

GPS Error Correction With Pseudorange Evaluation Using Three-Dimensional Maps

Lane-level vehicle self-localization is a challenging and significant issue arising in autonomous driving and driver-assistance systems. The Global Navigation Satellite System (GNSS) and onboard inertial sensor integration are among the solutions for vehicle self-localization. However, as the main source in the integration, GNSS positioning performance is severely degraded in urban canyons because of the effects of multipath and non-line-of-sight (NLOS) propagations. These GNSS positioning errors also decrease the performance of the integration. To reduce the negative effects caused by GNSS positioning error, this paper proposes to employ an innovative GNSS positioning technique with the aid of a 3-D building map in the integration. The GNSS positioning result is used as an observation, and this is integrated with the information from the onboard inertial sensor and vehicle speedometer in a Kalman filter framework. To achieve stable performance, this paper proposes to evaluate and consider the accuracy of the employed GNSS positioning method in dynamic integration. A series of experiments in different scenarios is conducted in an urban canyon, which can demonstrate the effectiveness of the proposed method using various evaluation and comparison processes.

GNSS/Onboard Inertial Sensor Integration With the Aid of 3-D Building Map for Lane-Level Vehicle Self-Localization in Urban Canyon

Current GPS positioning accuracy in urban areas is still unsatisfactory for various applications, including pedestrian navigation and autonomous driving. Due to the ineffectiveness of special corrector designs against non-line-of-sight (NLOS) reception, the research regarding NLOS signals has been increasing in the recent years. This study first develops an algorithm to detect NLOS signals from the pseudorange measurements by using a 3D building model, ray-tracing simulation, and known receiver position. According to the analysis of 24 h of collected NLOS data, a new finding is that NLOS pseudorange delay is highly correlated with the elevation angle of satellite instead of the received signal strength. Thus, we further propose an innovative NLOS model using two variables, the elevation angle and the distance between the receiver and building that reflect the NLOS. The proposed model is evaluated in both pseudorange and position domains. Based on the experiment results regarding pseudorange error, the difference between the proposed model and the collected NLOS measurement is very small. Finally, the proposed model is applied to a hypothesis-based positioning method and achieves about 6.3 m in terms of horizontal positioning accuracy, which is only slightly worse than the method applied with ray-tracing simulation.

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Analysis and modeling GPS NLOS effect in highly urbanized area

Insufficient localization accuracy of global navigation satellite system (GNSS) receivers is one of the challenges to implement advanced intelligent transportation system in highly urbanized areas. Multipath and non-line-of-sight (NLOS) effects strongly deteriorate GNSS positioning performance. This paper aims to train a classifier by supervised machine learning to separate the type of GNSS pseudorange measurement into three categories, clean, multipath and NLOS. Several features obtained or calculated from the GNSS raw data are evaluated. This paper also proposes a new feature to indicate the consistency between measurements of pseudorange and Doppler shift. According to the experiment result, about 75% of classification accuracy can be achieved using a support vector machine (SVM) classifier trained by the proposed feature and received signal strength.

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Li-Ta Hsu

Papers

3D building model-based pedestrian positioning method using GPS/GLONASS/QZSS and its reliability calculation

GPS Error Correction With Pseudorange Evaluation Using Three-Dimensional Maps

GNSS/Onboard Inertial Sensor Integration With the Aid of 3-D Building Map for Lane-Level Vehicle Self-Localization in Urban Canyon

Analysis and modeling GPS NLOS effect in highly urbanized area

GNSS multipath detection using a machine learning approach