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Number of GNSS satellites as a function of time. The US GPS became available without selective availability for civilian use in 2000. The Russian GLONASS was again at full operational capability in 2011. The European Galileo and Chinese BeiDou are both nearing completion.
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... 2010, the number of navigation satellites available has more than tripled, moving from GPS-only (USA) to the inclusion of a rebooted GLONASS (Russia) constellation in 2011 and now nearly completed Galileo (Europe) and BeiDou (China) constellations. This growth is shown in Figure 1 where there are now more than 100 operational navigation satellites in orbit. For the user, this implies a jump from 10 to 30+ satellites in view and has significant implications for on-road GNSS availability. ...
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... summary of these results is given in Table 5. The cumulative distribution of reported satellite visibility for the automotive GNSS and RT3000 are given in Figures 9 and 10, respectively. This shows the 68 th percentile number of satellites available for the automotive GNSS to be 16 with GPS + GLONASS + Galileo compared to 8 with the GPS + GLONASS RT3000. ...
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... part, this signifies the increased availability from a third constellation, adding approximately 8 more satellites above the horizon. However, the full story is given by the geospatial distribution of satellite visibility for the automotive GNSS and RT3000 shown in Figures 11 and 12. This shows a difference in how satellite visibility is reported between these two receivers. ...
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... geometry as described by Horizontal Dilution of Precision (HDOP) is also summarized in Table 5. The cumulative reported HDOP for the automotive GNSS and RT3000 are given in Figures 13 and 14, respectively. Similarly, the geospatial distributions are given in Figures 15 and 16. ...
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... cumulative reported HDOP for the automotive GNSS and RT3000 are given in Figures 13 and 14, respectively. Similarly, the geospatial distributions are given in Figures 15 and 16. These again show spatial uniformity. ...
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... are summarized in Table 6. A more detailed look at satellite visibility and HDOP is given in Figures 17 and 18, respectively. These show the spread of continuity loss to be small over the time intervals of interest for a given number of satellites or level of HDOP. ...
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... next component is characterization of outage times. The cumulative distribution of outage times for satellite visibility and HDOP is given in Figures 19 and 20. For a threshold of 6 satellites in view, this shows the median outage time to be 1.5 seconds and the 95 th percentile to be 26 seconds. ...
Citations
... The report suggests an integrity risk level of 10 −7 for autonomous road vehicles and automatic ship docking and 10 −6 for autonomous drones, as an example. In addition to the integrity requirement, continuity requirements are also being discussed regarding the critical operations of an AV, such as lane changing by AVs, overtaking by autonomous cars, and autonomous landings of unmanned aerial systems (UASs) (Reid, Pervez, et al., 2019). ...
... Continuity is a measure of the capability to continuously generate navigation solutions during operation without unexpected interruptions (ICAO, 1996). Previous work on requirements for autonomous cars has shown that continuity assurance is required during critical operations, including lane changes and overtaking (Reid, Pervez, et al., 2019). For drones, critical operations include landing and flight close to infrastructure. ...
... Situational awareness requirements in highly automated and fully autonomous vehicles are generally grouped into the following categories: (1) Road determination; (2) Lane determination; and (3) In-lane positioning. Each has their function which ranges from geofencing to path planning to lane keeping as discussed in [4]. Their relative difference is shown pictorially in Figure 3 which also indicates the approximate position error usually associated with these applications. ...
Autonomous vehicles are being deployed with a spectrum of capability, extending from driver assistance features for the highway in personal vehicles (SAE Level 2+) to fully autonomous fleet ride sharing services operating in complex city environments (SAE Level 4+). This spectrum of autonomy often operates in different physical environments with different degrees of assumed driver in-the-loop oversight and hence have very different system and subsystem requirements. At the heart of SAE Level 2 to 5 systems is localization and mapping, which ranges from road determination for feature geofencing or high-level routing, through lane determination for advanced driver assistance, to where-in-lane positioning for full vehicle control. We assess localization and mapping requirements for different levels of autonomy and supported features. This work provides a framework for system decomposition, including the level of redundancy needed to achieve the target level of safety. We examine several representative autonomous and assistance features and make recommendations on positioning requirements as well map georeferencing and information integrity.
... Furthermore, South Korea is also promoting projects related to an RNSS, known as the Korea positioning system (KPS) [1]. In this manner, the number of navigation satellites has been increasing rapidly owing to the increase in navigation satellite systems [2]. As of January 2022, the number of GNSS satellites in operation were 31 in GPS, 21 in GLONASS, 22 in Galileo, and 44 in BeiDou [3][4][5][6]. ...
... Equation (10) is comparable to Equation (5), which involved a similar process for the single GNSS. If uniformity is maintained in Equation (5), most of the elements of the nondiagonal matrix would become 0. In a dual GNSS, the matrix elements (1,4), (1,5), (2,4), and (2,5) are not 0. Therefore, it is difficult to perform a simple inverse matrix calculation, as in the case of a single GNSS. Equation (10) can also be expressed in the block matrix form, and the results are shown in Equation (11): ...
Increasing the number of satellites in a global navigation satellite system (GNSS) improves the positioning accuracy and increases availability. However, it reduces the positioning accuracy improvement rate and increases the calculation loads, which can cause battery usage problems in mobile devices using a GNSS. An appropriate satellite selection method is required. One current method entails the use of ideal satellite placement with respect to the minimum geometric dilution of precision (GDOP). In this study, the described ideal satellite placement with the minimum GDOP were divided in terms of the horizontal dilution of precision (HDOP) and vertical dilution of precision (VDOP). HDOP and VDOP were mathematically derived and analyzed. The derived formula was verified using simulations. The analysis was performed with actual dual GNSS satellite data. The satellites adjacent to the ideal placement were selected and the DOP was calculated. Simply selecting satellites closest to the ideal placement afforded large values for HDOP and VDOP. This issue was addressed using a satellite changing algorithm considering the dual GNSS, resulting in reduced values of the HDOP and VDOP.
... Concerning evaluation methods for localization performance, two different categories of approaches have been proposed by other researchers. The first category evaluates the performance of a localization system in relation to a high-quality reference position trajectory [7,13,14]. This approach allows for an efficient comparison of longitudinal and lateral position errors but suffers from the drawback that a position trajectory, if estimated by a localization system, may hardly be considered as ground-truth. ...
Automated driving systems are in need of accurate localization, i.e., achieving accuracies below 0.1 m at confidence levels above 95%. Although during the last decade numerous localization techniques have been proposed, a common methodology to validate their accuracies in relation to a ground-truth dataset is missing so far. This work aims at closing this gap by evaluating four different methods for validating localization accuracies of a vehicle’s position trajectory to different ground truths: (1) a static driving-path, (2) the lane-centerline of a high-definition (HD) map with validated accuracy, (3) localized vehicle body overlaps of the lane-boundaries of a HD map, and (4) longitudinal accuracy at stop points. The methods are evaluated using two localization test datasets, one acquired by an automated vehicle following a static driving path, being additionally equipped with roof-mounted localization systems, and a second dataset acquired from manually-driven connected vehicles. Results show the broad applicability of the approach for evaluating localization accuracy and reveal the pros and cons of the different methods and ground truths. Results also show the feasibility of achieving localization accuracies below 0.1 m at confidence levels up to 99.9% for high-quality localization systems, while at the same time demonstrate that such accuracies are still challenging to achieve.
... While this experiment was not designed with GNSS analysis in mind, the data still allows for insights into the current highway environment for GNSS performance. An analysis of this data was presented previously by Ford in 2019 [4]. ...
... This was calculated directly from the fields shown here, and thus does not account for the difference in reference points. The distance between the reference points can be calculated by estimating the lever arm between them [4]. ...
... Already, some analysis has been performed on this dataset, and findings for on-road GNSS accuracy, availability, and continuity were presented previously in [4]. Availability and continuity were broken down in terms of satellite visibility, satellite geometry, position type (RTK fixed, RTK float, or SPS), and RTK correction latency over the network. ...
There is a growing need for vehicle positioning information to support Advanced Driver Assistance Systems (ADAS), Connectivity (V2X), and Autonomous Driving (AD) features. These range from a need for road determination (5 meters), lane determination (1.5 meters), and determining where the vehicle is within the lane (0.3 meters). This paper presents the Ford Highway Driving RTK (Ford-HDR) dataset. This dataset includes nearly 30,000 km of data collected primarily on North American highways during a driving campaign designed to validate driver assistance features in 2018. This includes data from a representative automotive production GNSS used primarily for turn-by-turn navigation as well as an Inertial Navigation System (INS) which couples two survey-grade GNSS receivers with a tactical grade Inertial Measurement Unit (IMU) to act as ground truth. The latter utilized networked Real-Time Kinematic (RTK) GNSS corrections delivered over a cellular modem in real-time. This dataset is being released into the public domain to spark further research in the community.
... Positioning performance of several mass-market receivers along standardised road environments are explained in [17]. A RTK fixed solution is available at 50% of the time with a probability of continuity loss of 0.54 compared to 98% availability with a continuity loss probability of 0.045 for standard code phase positioning [18]. Different challenges with GNSS positioning in urban areas are explained; the performance improvement with multi-GNSS constellations has been demonstrated with both simulated and real data [19]. ...
We present analyses of Global Navigation Satellite System (GNSS) carrier phase observations in multiple kinematic scenarios for different receiver types. Multi-GNSS observations are recorded on high sensitivity and geodetic-grade receivers operating on a moving zero-baseline by conducting terrestrial urban and aerial flight experiments. The captured data is post-processed; carrier phase residuals are computed using the double difference (DD) concept. The estimated noise levels of carrier phases are analysed with respect to different parameters. We find DD noise levels for L1 carrier phase observations in the range of 1.4–2 mm (GPS, Global Positioning System), 2.8–4.6 mm (GLONASS, Global Navigation Satellite System), and 1.5–1.7 mm (Galileo) for geodetic receiver pairs. The noise level for high sensitivity receivers is at least higher by a factor of 2. For satellites elevating above 30∘, the dominant noise process is white phase noise. For the flight experiment, the elevation dependency of the noise is well described by the exponential model, while for the terrestrial urban experiment, multipath and diffraction effects overlay; hence no elevation dependency is found. For both experiments, a carrier-to-noise density ratio (C/N0) dependency for carrier phase DDs of GPS and Galileo is clearly visible with geodetic-grade receivers. In addition, C/N0 dependency is also visible for carrier phase DDs of GLONASS with geodetic-grade receivers for the terrestrial urban experiment.
... In 2019, Swift Navigation performed an on-road performance assessment of a state-of-the-art production GNSS system using the methodology outlined by Reid et al [43]. The positioning engine under test was Swift Navigation's Skylark, running on a Piksi Multi GNSS receiver, equipped with a Harxon antenna. ...
This paper surveys a number of recent developments in modern Global Navigation Satellite Systems (GNSS) and investigates the possible impact on autonomous driving architectures. Modern GNSS now consist of four independent global satellite constellations delivering modernized signals at multiple civil frequencies. New ground monitoring infrastructure, mathematical models, and internet services correct for errors in the GNSS signals at continent scale. Mass-market automotive-grade receiver chipsets are available at low Cost, Size, Weight, and Power (CSWaP). The result is that GNSS in 2020 delivers better than lane-level accurate localization with 99.99999% integrity guarantees at over 95% availability. In autonomous driving, SAE Level 2 partially autonomous vehicles are now available to consumers, capable of autonomously following lanes and performing basic maneuvers under human supervision. Furthermore, the first pilot programs of SAE Level 4 driverless vehicles are being demonstrated on public roads. However, autonomous driving is not a solved problem. GNSS can help. Specifically, incorporating high-integrity GNSS lane determination into vision-based architectures can unlock lane-level maneuvers and provide oversight to guarantee safety. Incorporating precision GNSS into LiDAR-based systems can unlock robustness and additional fallbacks for safety and utility. Lastly, GNSS provides interoperability through consistent timing and reference frames for future V2X scenarios.
A smart thinning operation refers to an advanced method of selecting and cutting trees to be thinned based on digitally captured forest information. In smart thinning operations, workers use the coordinates of individual trees to navigate to the target trees for thinning. However, it is difficult to accurately locate individual trees in a forest stand covered with a canopy, necessitating a precise real-time positioning system that can be used in the forest. Therefore, this study aimed to evaluate the applicability of the global navigation satellite system real-time kinematic (GNSS-RTK) device in a forest stand through analysis of its positioning accuracy within the forest environment and evaluation of the operational range of the single-baseline RTK based on analysis of the positioning precision and radio signal strength index (RSSI) change with increasing distance from the base station. The results showed that the root mean square error (RMSE) of the horizontal positioning error was highly accurate, with an average of 0.26 m in Larix kaempferi stands and 0.48 m in Pinus koraiensis stands. The RSSI decreased to a minimum of −103.3 dBm within 1 km of distance from the base station; however, this had no significant impact on the horizontal positioning precision. The conclusion is that the GNSS-RTK is suitable for use in smart thinning operations.
In recent years, most people use commercial integrated navigation systems to develop navigation algorithms. However, due to the different levels of sensors on the market, it is difficult to customize commercial systems and leads to limited development of navigation algorithms. Therefore, the purpose of this research is to develop a real-time integrated navigation system EGI-1000 (Embedded GNSS and INS) including software and hardware, and effectively reduce the cost with the commercial price. The real-time integrated navigation system EGI-1000 contains a navigation-grade IMU, IMU1000 and NovAtel OEM 7720 GNSS receiver module. In this research, the integration process can be divided into three parts. The first part is the integration of hardware, and the architecture diagram of the real-time integrated navigation system will be displayed. The second part is the pre-processing of data. In the multi-sensor time synchronization problem, this research will propose a method about cross-correlation to validate whether the timestamp of IMU data is delay. The last part is algorithm about fusing data from multiple sensors and motion constraints. Extended Kaman Filter (EKF) will be the core and motion constraints including Zero Velocity Update (ZUPT) and Non-Holonomic Constraints (NHC) are integrated in the Loosely Coupled (LC) scheme. The calibration of Inertial navigation Measurement Unit (IMU) will also be conducted to determine the parameter in algorithm. The results of the experiments will be shown in this paper. Both of hardware and navigation algorithm in the integrated navigation system of this research are used to conduct multiple experiment including open sky environments, GNSS challenging environments, and GNSS denied environment. In comparison with the reference data, the navigation accuracy of the developed integrated navigation system can achieve centimeter-level accuracy (“Active Control” level and “Where in lane” level) in open sky and GNSS challenging environments. According to the propagation error theory, the result in GNSS denied environment also meet the expected value. The navigation algorithm is also feasible for the commercial integrated navigation system.
At present, most Chinese automated vehicles companies are inclined to use BDS/GNSS and INS high-precision combined positioning receivers as main solution to achieve high-precision position perception of automated vehicles. But, there’re not unified performance requirements for the BDS/GNSS high-precision navigation system (HNS) of intelligent driving currently. It’s difficult to evaluate overall performance of this system based on a generally accepted principle. Research presented in this paper is to solve the problem of inconsistent, uncoordinated, and non-standard overall performance metrics, and promote development of the BDS/GNSS high-precision navigation system. Based on actual needs of L3–L4 automated vehicles, and more than 200,000 km data collected from the large-scale operation of SAIC MOTOR container truck, this paper puts forward to performance requirements of the BDS/GNSS high-precision navigation system such as accuracy, availability, continuity and integrity for automated vehicles in park scenes and high-speed scenes. And rationality of those performance requirements is verified via three test routes data.
