Hazard Warning Performance in Light of Vehicle Positioning Accuracy and Map-Less Approach Path Matching

2017 ◽  
Vol 10 (2) ◽  
pp. 290-300 ◽  
Author(s):  
Andreas Barthels ◽  
Christian Ress ◽  
Martin Wiecker ◽  
Manfred Müller
Keyword(s):  
Positioning Accuracy ◽  
Vehicle Positioning ◽  
Hazard Warning ◽  
Path Matching

scholarly journals A Mobile Robot Position Adjustment as a Fusion of Vision System and Wheels Odometry in Autonomous Track Driving

2021 ◽  
Vol 11 (10) ◽  
pp. 4496
Author(s):  
Jarosław Zwierzchowski ◽  
Dawid Pietrala ◽  
Jan Napieralski ◽  
Andrzej Napieralski
Keyword(s):  
Vision System ◽  
Measurement Unit ◽  
Primary System ◽  
Control Points ◽  
Positioning Accuracy ◽  
Internal Measurement ◽  
Exact Position ◽  
Accuracy Result ◽  
Vehicle Positioning ◽  
Robot Position

Autonomous mobile vehicles need advanced systems to determine their exact position in a certain coordinate system. For this purpose, the GPS and the vision system are the most often used. These systems have some disadvantages, for example, the GPS signal is unavailable in rooms and may be inaccurate, while the vision system is strongly dependent on the intensity of the recorded light. This paper assumes that the primary system for determining the position of the vehicle is wheel odometry joined with an IMU (Internal Measurement Unit) sensor, which task is to calculate all changes in the robot orientations, such as yaw rate. However, using only the results coming from the wheels system provides additive measurement error, which is most often the result of the wheels slippage and the IMU sensor drift. In the presented work, this error is reduced by using a vision system that constantly measures vehicle distances to markers located in its space. Additionally, the paper describes the fusion of signals from the vision system and the wheels odometry. Studies related to the positioning accuracy of the vehicle with both the vision system turned on and off are presented. The laboratory averaged positioning accuracy result was reduced from 0.32 m to 0.13 m, with ensuring that the vehicle wheels did not experience slippage. The paper also describes the performance of the system during a real track driven, where the assumption was not to use the GPS geolocation system. In this case, the vision system assisted in the vehicle positioning and an accuracy of 0.2 m was achieved at the control points.

scholarly journals Anomaly Detection for Urban Vehicle GNSS Observation with a Hybrid Machine Learning System

2020 ◽  
Vol 12 (6) ◽  
pp. 971 ◽  
Author(s):  
Yan Xia ◽  
Shuguo Pan ◽  
Xiaolin Meng ◽  
Wang Gao ◽  
Fei Ye ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Urban Areas ◽  
Single Point ◽  
Learning System ◽  
Classification Rule ◽  
Detection Methods ◽  
Positioning Accuracy ◽  
Building Models ◽  
Vehicle Positioning

In urban areas, the accuracy and reliability of global navigation satellite systems (GNSS) vehicle positioning decline due to substantial non-line-of-sight (NLOS) signals and multipath effects. Recently, positioning enhancement approaches with supervised GNSS signal type classification based on 3D building model-aided labelling have received widespread attention. Despite the reduced computing costs and improved real-time performance, the strict requirements of 3D building models on accuracy and timeliness limit the popularization of the technology to some extent. Meanwhile, the diversity of anomalous observation sources is beyond the reach of NLOS/multipath detection methods. This paper attempts to construct an alternative framework for quality identification of GNSS observations combining clustering-based anomaly detection and supervised classification, in which the hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm is used to label the offline dataset as normal and anomalous observations without the aid of 3D building models, and the supervised classifier in the online system learns the classification rule for real-time anomaly detection. The experimental results based on the measured vehicle GPS/BeiDou data show that after excluding anomalous observations the single point positioning accuracy of the offline dataset is improved by 87.0%, 45.9%, and 69.6% in the east, north, and up directions, respectively, and the positioning accuracy of two online datasets is improved by 48.4%/45.7%, 39.6%/63.3%, and 49.6%/49.1% in the three directions. Through a large number of comparative experiments and discussion on key issues, it is certified that the proposed method is highly feasible and has great potential in the practical application of urban GNSS vehicle positioning.

scholarly journals A New Faulty GNSS Measurement Detection and Exclusion Algorithm for Urban Vehicle Positioning

2021 ◽  
Vol 13 (11) ◽  
pp. 2117
Author(s):  
Qi Cheng ◽  
Ping Chen ◽  
Rui Sun ◽  
Junhui Wang ◽  
Yi Mao ◽  
...  
Keyword(s):  
Fault Detection ◽  
Real Time ◽  
Urban Areas ◽  
Least Square ◽  
Global Navigation Satellite Systems ◽  
Positioning Accuracy ◽  
Detection Scheme ◽  
Multiple Faults ◽  
Ground Vehicle ◽  
Vehicle Positioning

The performance requirements for Global Navigation Satellite Systems (GNSS) are becoming more demanding as the range of mission-critical vehicular applications, including the Unmanned Aerial Vehicle (UAV) and ground vehicle-based applications, increases. However, the accuracy and reliability of GNSS in some environments, such as in urban areas, are often affected by non-line-of-sight (NLOS) signals and multipath effects. It is therefore essential to develop an effective fault detection scheme that can be applied to GNSS observations so as to ensure that the vehicle positioning can be calculated with a high accuracy. In this paper, we propose an online dataset based faulty GNSS measurement detection and exclusion algorithm for vehicle positioning that takes account of the NLOS/multipath affected scenarios. The proposed algorithm enables a real-time online dataset based fault detection and exclusion scheme, which makes it possible to detect multiple faults in different satellites simultaneously and accurately, thereby allowing real-time quality control of GNSS measurements in dynamic urban positioning applications. The algorithm was tested with simulated/artificial step errors in various scenarios in the measured pseudoranges from a dataset acquired from a UAV in an open area. Furthermore, a real-world test was also conducted with a ground-vehicle driving in a dense urban environment to validate the practical efficiency of the proposed algorithm. The UAV based simulation exhibits a fault detection rate of 100% for both single and multi-satellite fault scenarios, with the horizontal positioning accuracy improved to about 1 metre from tens of metres after fault detection and exclusion. The ground vehicle-based real test shows an overall improvement of 26.1% in 3D positioning accuracy in an urban area compared to the traditional least square method.

scholarly journals Vehicle Location Algorithm Based on Federated Learning and Smart Phone in GNSS Low Sampling Rate Scene

2021 ◽  
Vol 2066 (1) ◽  
pp. 012052
Author(s):  
Wenbiao Guo ◽  
Hang Li ◽  
Feng Yin ◽  
Bo Ai
Keyword(s):  
Learning Algorithm ◽  
Sampling Rate ◽  
Smart Phone ◽  
Satellite System ◽  
Signal Loss ◽  
User Privacy ◽  
Positioning Accuracy ◽  
Positioning Method ◽  
Vehicle Positioning ◽  
Global Navigation Satellite

Abstract In the existing vehicle positioning system based on Global Navigation Satellite System/Inertial Navigation System (GNSS/INS), when the GNSS signal is lost, the error accumulated by using only the INS will damage the positioning accuracy. In order to improve the accuracy, this paper proposed a positioning method based on data-driven and learning models, which utilized distributed data sets to collaboratively construct accurate positioning models through federated fusion algorithms without sacrificing user privacy. In the field scenarios of IID and Non-IID types, this paper compared the performance of INS and the existing two typical methods, DeepSense and PVAUA, it is verified that the two federated learning algorithm models constructed had higher positioning accuracy in different scenarios and different GNSS signal loss durations. The results were analyzed.

scholarly journals MIMU/Odometer Fusion with State Constraints for Vehicle Positioning during BeiDou Signal Outage: Testing and Results

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2302
Author(s):  
Kai Zhu ◽  
Xuan Guo ◽  
Changhui Jiang ◽  
Yujingyang Xue ◽  
Yuanjun Li ◽  
...  
Keyword(s):  
Navigation System ◽  
Urban Areas ◽  
State Constraints ◽  
Rapid Development ◽  
Tall Buildings ◽  
Measurement Unit ◽  
Integrated Navigation ◽  
Positioning Accuracy ◽  
Positioning Errors ◽  
Vehicle Positioning

With the rapid development of autonomous vehicles, the demand for reliable positioning results is urgent. Currently, the ground vehicles heavily depend on the Global Navigation Satellite System (GNSS) and the Inertial Navigation System (INS) providing reliable and continuous navigation solutions. In dense urban areas, especially narrow streets with tall buildings, the GNSS signals are possibly blocked by the surrounding tall buildings, and under this condition, the geometry distribution of the in-view satellites is very poor, and the None-Line-Of-Sight (NLOS) and Multipath (MP) heavily affects the positioning accuracy. Further, the INS positioning errors will quickly diverge over time without the GNSS correction. Aiming at improving the position accuracy under signal challenging environment, in this paper, we developed an MIMU(Micro Inertial Measurement Unit)/Odometer integration system with vehicle state constraints (MO-C) for improving the vehicle positioning accuracy without GNSS. MIMU/Odometer integration model and the constrained measurements are given in detail. Several field tests were carried out for evaluating and assessing the MO-C system. The experiments were divided into two parts, firstly, field testing with data post-processing and real-time processing was carried out for fully assessing the performance of the MO-C system. Secondly, the MO-C was implemented in the BeiDou Satellite Navigation System (BDS)/integrated navigation system (INS) for evaluating the MO-C performance during the BDS signal outage. The MIMU standalone positioning accuracy was compared with that from the MIMU/Odometer integration (MO), MO-C and MIMU with constraints (M-C) for assessing the Odometer, and the influence of the constraint on the positioning errors reduction. The results showed that the latitude and longitude errors could be suppressed with Odometer assisting, and the height errors were suppressed while the state constraints were included.

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