scholarly journals Improvement of Reliability Determination Performance of Real Time Kinematic Solutions Using Height Trajectory

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 657
Author(s):  
Aoki Takanose ◽  
Yoshiki Atsumi ◽  
Kanamu Takikawa ◽  
Junichi Meguro
Keyword(s):  
Real Time ◽  
Urban Areas ◽  
Estimation Method ◽  
Tall Buildings ◽  
Autonomous Driving ◽  
Reliability Estimation ◽  
Height Variation ◽  
Position Information ◽  
Real Time Kinematic ◽  
Evaluation Test

Autonomous driving support systems and self-driving cars require the determination of reliable vehicle positions with high accuracy. The real time kinematic (RTK) algorithm with global navigation satellite system (GNSS) is generally employed to obtain highly accurate position information. Because RTK can estimate the fix solution, which is a centimeter-level positioning solution, it is also used as an indicator of the position reliability. However, in urban areas, the degradation of the GNSS signal environment poses a challenge. Multipath noise caused by surrounding tall buildings degrades the positioning accuracy. This leads to large errors in the fix solution, which is used as a measure of reliability. We propose a novel position reliability estimation method by considering two factors; one is that GNSS errors are more likely to occur in the height than in the plane direction; the other is that the height variation of the actual vehicle travel path is small compared to the amount of movement in the horizontal directions. Based on these considerations, we proposed a method to detect a reliable fix solution by estimating the height variation during driving. To verify the effectiveness of the proposed method, an evaluation test was conducted in an urban area of Tokyo. According to the evaluation test, a reliability judgment rate of 99% was achieved in an urban environment, and a plane accuracy of less than 0.3 m in RMS was achieved. The results indicate that the accuracy of the proposed method is higher than that of the conventional fix solution, demonstratingits effectiveness.

scholarly journals High-Accuracy Real-Time Kinematic Positioning with Multiple Rover Receivers Sharing Common Clock

2021 ◽  
Vol 13 (4) ◽  
pp. 823
Author(s):  
Lin Zhao ◽  
Jiachang Jiang ◽  
Liang Li ◽  
Chun Jia ◽  
Jianhua Cheng
Keyword(s):  
Real Time ◽  
Ambiguity Resolution ◽  
Temporal Stability ◽  
Estimation Method ◽  
Satellite System ◽  
Integer Ambiguity ◽  
Dual Frequency ◽  
Kinematic Positioning ◽  
Real Time Kinematic ◽  
Satellite Visibility

Since the traditional real-time kinematic positioning method is limited by the reduced satellite visibility from the deprived navigational environments, we, therefore, propose an improved RTK method with multiple rover receivers sharing a common clock. The proposed method can enhance observational redundancy by blending the observations from each rover receiver together so that the model strength will be improved. Integer ambiguity resolution of the proposed method is challenged in the presence of several inter-receiver biases (IRB). The IRB including inter-receiver code bias (IRCB) and inter-receiver phase bias (IRPB) is calibrated by the pre-estimation method because of their temporal stability. Multiple BeiDou Navigation Satellite System (BDS) dual-frequency datasets are collected to test the proposed method. The experimental results have shown that the IRCB and IRPB under the common clock mode are sufficiently stable for the ambiguity resolution. Compared with the traditional method, the ambiguity resolution success rate and positioning accuracy of the proposed method can be improved by 19.5% and 46.4% in the restricted satellite visibility environments.

scholarly journals A Survey on Novel Estimation Approach of Motion Controllers for Self-Driving Cars

2021 ◽  
Vol 2 (4) ◽  
pp. 211-219
Author(s):  
Vinothkanna R
Keyword(s):  
Motion Planning ◽  
Real Time ◽  
Autonomous Vehicle ◽  
Estimation Method ◽  
Autonomous Driving ◽  
Estimation Methods ◽  
Real Time Traffic ◽  
Traffic Uncertainty ◽  
Research Article ◽  
Uncertainty Control

The motion planning framework is one of the challenging tasks in autonomous driving cars. During motion planning, predicting of trajectory is computed by Gaussian propagation. Recently, the localization uncertainty control will be estimating by Gaussian framework. This estimation suffers from real time constraint distribution for (Global Positioning System) GPS error. In this research article compared novel motion planning methods and concluding the suitable estimating algorithm depends on the two different real time traffic conditions. One is the realistic unusual traffic and complex target is another one. The real time platform is used to measure the several estimation methods for motion planning. Our research article is that comparing novel estimation methods in two different real time environments and an identifying better estimation method for that. Our suggesting idea is that the autonomous vehicle uncertainty control is estimating by modified version of action based coarse trajectory planning. Our suggesting framework permits the planner to avoid complex and unusual traffic (uncertainty condition) efficiently. Our proposed case studies offer to choose effectiveness framework for complex mode of surrounding environment.

scholarly journals Real-Time Road Lane Detection in Urban Areas Using LiDAR Data

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 276 ◽  
Author(s):  
Jiyoung Jung ◽  
Sung-Ho Bae
Keyword(s):  
Real Time ◽  
Urban Areas ◽  
Autonomous Driving ◽  
Lane Detection ◽  
Detection Methods ◽  
Lidar Data ◽  
The Road ◽  
Road Signs ◽  
Probe Vehicle ◽  
Map Generation

The generation of digital maps with lane-level resolution is rapidly becoming a necessity, as semi- or fully-autonomous driving vehicles are now commercially available. In this paper, we present a practical real-time working prototype for road lane detection using LiDAR data, which can be further extended to automatic lane-level map generation. Conventional lane detection methods are limited to simple road conditions and are not suitable for complex urban roads with various road signs on the ground. Given a 3D point cloud scanned by a 3D LiDAR sensor, we categorized the points of the drivable region and distinguished the points of the road signs on the ground. Then, we developed an expectation-maximization method to detect parallel lines and update the 3D line parameters in real time, as the probe vehicle equipped with the LiDAR sensor moved forward. The detected and recorded line parameters were integrated to build a lane-level digital map with the help of a GPS/INS sensor. The proposed system was tested to generate accurate lane-level maps of two complex urban routes. The experimental results showed that the proposed system was fast and practical in terms of effectively detecting road lines and generating lane-level maps.

scholarly journals The Integration of GPS/BDS Real-Time Kinematic Positioning and Visual–Inertial Odometry Based on Smartphones

2021 ◽  
Vol 10 (10) ◽  
pp. 699
Author(s):  
Zun Niu ◽  
Fugui Guo ◽  
Qiangqiang Shuai ◽  
Guangchen Li ◽  
Bocheng Zhu
Keyword(s):  
Real Time ◽  
Urban Areas ◽  
Smartphone Application ◽  
Satellite System ◽  
Kinematic Positioning ◽  
Multiple Sensors ◽  
Real Time Kinematic ◽  
Global Navigation Satellite ◽  
Improved Algorithm

The real-time kinematic positioning technique (RTK) and visual–inertial odometry (VIO) are both promising positioning technologies. However, RTK degrades in GNSS-hostile areas, where global navigation satellite system (GNSS) signals are reflected and blocked, while VIO is affected by long-term drift. The integration of RTK and VIO can improve the accuracy and robustness of positioning. In recent years, smartphones equipped with multiple sensors have become commodities and can provide measurements for integrating RTK and VIO. This paper verifies the feasibility of integrating RTK and VIO using smartphones, and we propose an improved algorithm to integrate RTK and VIO with better performance. We began by developing an Android smartphone application for data collection and then wrote a Python program to convert the data to a robot operating system (ROS) bag. Next, we established two ROS nodes to calculate the RTK results and accomplish the integration. Finally, we conducted experiments in urban areas to assess the integration of RTK and VIO based on smartphones. The results demonstrate that the integration improves the accuracy and robustness of positioning and that our improved algorithm reduces altitude deviation. Our work can aid navigation and positioning research, which is the reason why we open source the majority of the codes at our GitHub.

An expert system for real-time well field management

2012 ◽  
Vol 12 (5) ◽  
pp. 699-706 ◽  
Author(s):  
B. S. Marti ◽  
G. Bauser ◽  
F. Stauffer ◽  
U. Kuhlmann ◽  
H.-P. Kaiser ◽  
...  
Keyword(s):  
Drinking Water ◽  
Expert System ◽  
Knowledge Base ◽  
Real Time ◽  
Urban Areas ◽  
Industrial Sites ◽  
Water Wells ◽  
Well Field ◽  
Field Management ◽  
Historical Management

Well field management in urban areas faces challenges such as pollution from old waste deposits and former industrial sites, pollution from chemical accidents along transport lines or in industry, or diffuse pollution from leaking sewers. One possibility to protect the drinking water of a well field is the maintenance of a hydraulic barrier between the potentially polluted and the clean water. An example is the Hardhof well field in Zurich, Switzerland. This paper presents the methodology for a simple and fast expert system (ES), applies it to the Hardhof well field, and compares its performance to the historical management method of the Hardhof well field. Although the ES is quite simplistic it considerably improves the water quality in the drinking water wells. The ES knowledge base is crucial for successful management application. Therefore, a periodic update of the knowledge base is suggested for the real-time application of the ES.

JOINT ESTIMATION OF THE TRANSMISSIBILITY AND SEVERITY OF EBOLA VIRUS DISEASE IN REAL TIME

2017 ◽  
Vol 25 (04) ◽  
pp. 587-603 ◽  
Author(s):  
YUSUKE ASAI ◽  
HIROSHI NISHIURA
Keyword(s):  
Real Time ◽  
Ebola Virus ◽  
Virus Disease ◽  
Critical Role ◽  
Estimation Method ◽  
Case Fatality ◽  
Ascertainment Bias ◽  
Joint Estimation ◽  
Ebola Virus Disease ◽  
Strong Impact

The effective reproduction number [Formula: see text], the average number of secondary cases that are generated by a single primary case at calendar time [Formula: see text], plays a critical role in interpreting the temporal transmission dynamics of an infectious disease epidemic, while the case fatality risk (CFR) is an indispensable measure of the severity of disease. In many instances, [Formula: see text] is estimated using the reported number of cases (i.e., the incidence data), but such report often does not arrive on time, and moreover, the rate of diagnosis could change as a function of time, especially if we handle diseases that involve substantial number of asymptomatic and mild infections and large outbreaks that go beyond the local capacity of reporting. In addition, CFR is well known to be prone to ascertainment bias, often erroneously overestimated. In this paper, we propose a joint estimation method of [Formula: see text] and CFR of Ebola virus disease (EVD), analyzing the early epidemic data of EVD from March to October 2014 and addressing the ascertainment bias in real time. To assess the reliability of the proposed method, coverage probabilities were computed. When ascertainment effort plays a role in interpreting the epidemiological dynamics, it is useful to analyze not only reported (confirmed or suspected) cases, but also the temporal distribution of deceased individuals to avoid any strong impact of time dependent changes in diagnosis and reporting.

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