=Paper=
{{Paper
|id=Vol-2626/paper4
|storemode=property
|title=Challenges in Characterization of GNSS Precise Positioning Systems for Automotive
|pdfUrl=https://ceur-ws.org/Vol-2626/paper4.pdf
|volume=Vol-2626
|authors=Cristiano Pendão,André Ferreira,Adriano Moreira,César Martins,Hélder Silva
|dblpUrl=https://dblp.org/rec/conf/icl-gnss/PendaoFMMS20
}}
==Challenges in Characterization of GNSS Precise Positioning Systems for Automotive==
https://ceur-ws.org/Vol-2626/paper4.pdf
Challenges in Characterization of GNSS Precise
Positioning Systems for Automotive
Cristiano Pendãoa , André G. Ferreiraa , Adriano Moreiraa , César Martinsc and
Hélder Silvab
a
Algoritmi Research Center, University of Minho - Portugal
b
Center for MicroElectromechanical Systems, University of Minho - Portugal
c
Bosch Car Multimedia Portugal, S.A., Braga - Portugal
Abstract
Autonomous driving is currently one of the main focuses of attention in the automotive industry. A
requirement for efficient and safe driving of autonomous vehicles is the ability to precisely pinpoint
the location of the vehicle, in the decimeter- to centimeter-level on a global scale. GNSS is expected
to play a major role in providing accurate absolute and global positioning, yet many challenges
arise in dense urban environments due to lack of line-of-sight to satellites and multi-path, decreasing
availability and accuracy. Also, the position accuracy announced by GNSS receiver manufacturers
is rather optimistic, typically obtained in best-case scenarios. However, this is rarely encountered
in real-world driving conditions, especially in urban areas, leading to a mismatch between receiver
specification and real world performance. This paper provides a systematic study regarding the
requirements, methods, and solutions available for the characterization/evaluation of a GNSS po-
sitioning system in real world driving conditions. An architecture for a precise Automotive Global
Reference System (centimeter-level), able to characterize a decimeter-level accuracy GNSS position-
ing system in dynamic conditions, is proposed. To the best of authors’ knowledge, such a study is
not available in the literature.
Keywords
Autonomous Driving, Precise Positioning, GNSS Receiver Characterization, Reference System
1. Introduction
GNSS positioning systems have been providing a wide range of services to the population,
industry and governmental organizations for many years. The improvements in GNSS tech-
nology have been significant in the past decade, with a faster time-to-first-fix accurate position
acquisition, improved receiver sensitivity, more constellations and functional satellites, as well
as improved signals [1, 2, 3]. These improvements create the opportunity for the development
of new receivers with support for multiple constellations and multiple signal bands, making the
GNSS one of the most scalable and reliable technologies for global high accuracy positioning.
In Autonomous Driving (AD), GNSS is expected to play a major role in providing accurate
absolute positioning, with other technologies (e.g. LiDAR, Cameras) providing relative posi-
tioning [4, 5]. In a report released by the European GNSS Agency, the requirements for AD are
defined as better than 20 cm of horizontal accuracy with 95% confidence [4]. The performance
ICL-GNSS 2020 WiP Proceedings, June 02–04, 2020, Tampere, Finland
email: cpendao@dsi.uminho.pt (C. Pendão); hdsilva@dei.uminho.pt (H. Silva)
orcid: 0000-0002-4563-7414 (C. Pendão); 0000-0003-3204-5468 (A.G. Ferreira); 0000-0002-8967-118X (A.
Moreira); 0000-0002-6570-6501 (H. Silva)
⃝
c 2020 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073 CEUR Workshop Proceedings (CEUR-WS.org)
of autonomous vehicle systems will benefit greatly from high accuracy GNSS systems, for ex-
ample for safety critical applications, such as forward collision warning (V2X) [4]. However,
there are many challenges to solve in order to achieve reliable decimeter or better accuracy to
support AD. A GNSS receiver collects and processes signals subjected to several impairments
(e.g. troposphere and ionosphere interference), as well as multi-path effects, where the signals
are reflected from nearby objects and reach the receiver through multiple and indirect trajecto-
ries. When the receiver’s line-of-sight is blocked, the positioning accuracy is severely degraded.
This problem has significant impact in AD applications, since vehicles are frequently moving
through tunnels and in large urban areas, where the GNSS signals are blocked by tall buildings
(urban canyons) [4]. Therefore, the receiver architecture, positioning algorithms and correction
systems play a major role in mitigating these effects in order to obtain high accuracy.
GNSS manufacturers typically announce accuracies obtained in controlled conditions, with
direct line-of-sight to a clear sky which is the best-case scenario and not realistic for applications
with demanding requirements. Real-world driving conditions are far more challenging for GNSS
signals than the best-case scenario. AD is expected to outperform a human-controlled vehicle
in terms of reliability and security. Since GNSS positioning is of utmost importance in this
context, a full characterization of the system accuracy in real-world is mandatory to guarantee
that the system is capable of providing centimeter- or decimeter-level accuracy 24/7.
To evaluate a GNSS receiver with decimetre positioning accuracy (e.g. 20 cm, as previously
defined) a reference system should fulfill the following requirements: (1) provide reliable abso-
lute ground truth with one order of magnitude better accuracy (e.g. 2 cm, 95% confidence);
(2) maintain high performance (e.g. 99.9% availability [4]) in real-world driving conditions
(e.g. highway speeds, tunnels, urban canyons); (3) globally available.
The main contribution of this paper is a systematic study on the challenges and possible
solutions for a suitable reference system able to meet the requirements for accurate character-
ization and evaluation of precise positioning GNSS systems in real world driving conditions,
which the authors could not find in the current literature. Section 2 describes the main param-
eters for a GNSS receiver characterization. Section 3 and 4, discuss approaches to improve the
performance of GNSS positioning, in order to obtain suitable ground-truth to evaluate high
accuracy systems in dynamic conditions. Section 5 presents the architecture for the proposed
Global Reference System.
2. GNSS Receiver Characterization Parameters
To characterize a GNSS receiver it is necessary to obtain a set of parameters that provide
information on the performance of the device when capturing and processing GNSS signals.
There are three dimensions (Fig. 1) where the performance of the receiver is tested: time,
signal power and accuracy [6].
The time dimension includes the Time-To-First-Fix (TTFF) under different conditions (cold
start: no information about the satellite position and time; warm start: valid almanac infor-
mation, no ephemeris information, position is within 100 km of last fix and time is known; hot
start: all information is known and position is within 100 km of last fix) [6]. Reacquisition time
is also an important parameter for automotive applications. This measures the time necessary
for a position fix to be obtained after a momentary signal outage, such as when a vehicle enters
a tunnel. Faster reacquisition times enable the navigation system to provide driving directions
immediately after the end of a tunnel.
RECEIVER CHARACTERIZATION
TIME SIGNAL POWER ACCURACY Predictable
1 1
Time-to-first-fix Acquisition Sensibility Static Accuracy Repeatable
Reacquisition Time Tracking Sensibility Dynamic Accuracy Relative
1 Initial conditions: Cold, Warm and Hot-start
Figure 1: Fundamental GNSS Receiver Characterization Parameters
In the power domain, the minimum power level of the signals is typically evaluated at
different stages of the signal processing. The acquisition sensitivity parameter is the minimum
power level such that the correlators are able to search and identify a signal, which is typically
below noise level, until a first fix is obtained. This parameter is also dependent on the initial
conditions (cold, warm and hot-start) of the receiver, since knowledge of which satellites to
search will speed-up the process. Tracking sensitivity is the minimum power level that allows
the receiver to maintain lock of the signal.
The accuracy is divided in two components: static and dynamic. The static parameter can
be subdivided into three categories: predictable, repeatable and relative. Static predictable is
the accuracy of a receiver’s position solution with respect to a known fixed point of a map.
Static repeatable is the accuracy with which a user can return to a position whose coordinates
have been measured previously with the same receiver under the same conditions (precision of
the receiver). Static relative is the accuracy with which a user can measure position relative
to another user with the same receiver in the same conditions. Dynamic accuracy measures
the receiver ability to pinpoint the true position of the vehicle in a map, when the vehicle is
undergoing motion in any of the axes.
Many of the characterization parameters described above are obtained in laboratorial envi-
ronment using two types of devices: GNSS simulators (e.g. from Spirent and Rohde & Schwarz)
and Record & Replay (R&R) systems (e.g. from Spirent and RaceLogic). The former simulates
one or more constellations of satellites, by generating the signals that would be observed by a
GNSS receiver in a specific location on earth. The latter records real GNSS data, which can
then be reproduced for each receiver under test.
Simulation typically does not address highly complex scenarios. When multi-path simulation
is offered, it is often a simplistic test for a very specific use case. The influence of moving
objects (e.g. cars, trucks) and the properties of the materials surrounding the receiver (e.g.
trees, buildings) are also absent. Despite allowing testing GNSS receivers under very limited
conditions, this type of devices are expensive (100-300Ke). With R&R systems, data must
first be collected in different conditions (e.g. in open area, intermediate/light urban area and
urban area [4]). Compared to the GNSS simulation, the R&R system has lower flexibility since
new data must be collected in order to test different scenarios. However, this type of device
replicates real signals, which allows benchmarking different receivers with real conditions and
the cost is typically significantly lower (10K-30Ke).
While timing and power characterization are well covered by these devices, the same cannot
be said for the accuracy parameters. On one hand, the GNSS simulation generates a given
coordinate precisely, allowing for direct accuracy characterization, yet presents a simplistic
scenario when implementing interference and multi-path. On the other hand, a R&R system
captures interferences and replays them, although the exact position of recording may not be
well accounted for, especially in dynamic scenarios. In addition, another GNSS receiver is
needed, one with higher accuracy than the device under test, in order to characterize accuracy
using the R&R system.
However, there is a fundamental issue regarding receiver characterization using only GNSS
signals. As mentioned before, the position being estimated is affected by multiple external
factors, and ultimately it may contain significant errors, even for the high accuracy GNSS
receiver used as reference. Therefore, comparing against another higher quality GNSS receiver,
cannot guarantee that the real accuracy is being characterized. Ideally, the system being used
to characterize accuracy should be immune to the error sources that affect the device under
test. However, considering the requirements defined (e.g., 2 cm 95%, 99% availability and
global coverage) for this type of reference system, this is extremely difficult to achieve.
Dynamic accuracy is definitely the most challenging evaluation parameter, but at the same
time one of the most important in the automotive context. This problem can be addressed by
using GNSS Augmentation and merging GNSS with information from other sensors (e.g., In-
ertial Measurement Units (IMUs), Odometers, etc), in order to obtain higher accuracy ground
truth in dynamic conditions. The following sections introduce the approaches of GNSS Aug-
mentation and GNSS fusion with other sensors.
3. GNSS Augmentation
The typical accuracy of a GNSS system (2-3 m in open sky conditions [7, 5]) can be drastically
improved using correction data obtained with GNSS Augmentation.
The augmentation can be based on a single reference station or on a network of reference
stations, providing corrections with different coverage and accuracy (up to centimetre-level).
With these approaches, satellite position, clock and atmospheric errors can be greatly min-
imized, leading to higher navigation performance (improved accuracy, integrity, continuity,
availability). However, not all GNSS errors can be eliminated (e.g. multi-path errors caused
by skyscrapers). Depending on the source of external information used, the augmentation can
be classified as Satellite-Based Augmentation Systems (SBAS) or Ground-Based Augmentation
Systems (GBAS). In SBAS [8, 9] GNSS measurements are collected by reference stations (e.g.
located across an entire continent), and computed in a central system to extract differential
corrections and integrity messages. The correction parameters are broadcast using geosta-
tionary satellites, usually providing wide-area or regional augmentation. Many regions have
their own SBAS system (e.g. European Union (EGNOS)), with many others in development.
GBASs [10] are used to improve the GNSS service in a limited area (e.g. to support landing
and take off at airports [11]). The main objective of a GBAS is to provide integrity assurance,
but it is also able to provide accuracy better than 1 m. Four or more GNSS receivers are
used to collect pseudo-ranges for the primary satellites, computing and broadcasting integrity
information.
More recently [12], another classification used for GNSS augmentation systems is Observation
State Representation (OSR) and State Space Representation (SSR) (Fig. 2). In OSR, the
corrections provided are in the form of differential observations that are used by the rover
(vehicle’s GNSS receiver) to correct local errors, where the error is a lump sum of all sources
affecting the distance measurement. In SSR, the corrections are provided as parameters that
model the various errors affecting the distance measurement.
Standard Positioning Service (SPS)
OSR-Based SSR-Based
Double differences of code
Regional network of ref. stations.
observations from a reference station
DGNSS SBAS Correction services through L-Band,
and a rover. Requires a communications
based on the code.
link between the 2 receivers.
Global network of reference stations.
Double differences of code and phase Correction services (orbits + clocks)
observations from a reference station PPP
RTK through L-Band or FTP, based on the
and a rover. Requires a communications code and carrier phase.
link between the 2 receivers.
Global network of reference stations.
Correction services (orbits + clocks +
PPP-AR
phase bias) through L-Band or FTP,
An NRTK Server processes the double
based on the code and carrier phase.
differences of code and carrier phase
observations from a local network of NRTK Local/Regional network of reference
reference stations. Corrections (e.g. FKP, stations. Correction services (orbits +
VRS, MAC) via a communication link PPP-RTK clocks + phase bias + troposphere +
ionosphere) through L-Band or FTP,
based on the code and carrier phase.
TIMELINE
Figure 2: Correction Services Overview
3.1. OSR-Based
Differential GNSS (DGNSS) [2] is an augmentation system based on a network of ground
reference stations, that broadcast differential information to the rover. This type of system only
provides position accuracy improvements, not assuring integrity. The correction parameters
are also typically broadcasted using short-range ground transmitters. The classic DGNSS
technique (Fig. 3) finds the deviation between the accurately known reference station and the
currently estimated positions. Based on this deviation, corrections to the measured pseudo-
ranges are computed and used to correct the rover’s position. The achieved accuracy is up to
1 m for distances in the range of tens of km.
GNSS SATELLITE
GPS, Galileo, Other
TRUE POSITION GNSS POSITION
GNSS REFERENCE STATION
With accurately known position
CORRECTION PARAMETERS
For each satellite
POSITION ERROR 20 - 50 cm
CORRECTED POSITION GNSS POSITION
USER GNSS RECEIVER
With differential correction
BASELINE < 50 Km
Figure 3: Classic Differential GNSS (DGNSS)
With Real-Time Kinematic (RTK) [13] a reference station provides information about the
pseudo-range and carrier phase measurements. RTK can provide real-time corrections to the
rover (for distances between 10-20 km), being possible to achieve centimetre-level accuracy
(<5 cm), being frequently used for example for land surveying and Unmanned Aerial Vehicle
navigation. Using a network of base stations (Network RTK (NRTK)) the working distance
increases to 50-70 km, by mitigating atmospheric dependent effects over distance. With NRTK
using OSR, the rover must be within (or at least near) the reference network. The Wide-Area
RTK (WARTK) technique allows the extension of local services to wide-area scale (400 - 1000
km), using a permanent reference station network, with accuracies between 5 and 10 cm.
3.2. SSR-Based
A Precise Point Positioning (PPP) system [14, 15, 16] models GNSS errors using a network of
ground reference stations, and transmits the corrections for the different signals broadcasted
by each satellite (Fig. 4). The PPP system architecture is similar to a SBAS system, however
the correction data can be broadcasted to the rover via satellite or Internet. PPP can be used
worldwide, while an SBAS system coverage is regional or continental.
GEO SATELLITE
Geostationary
GNSS SATELLITE CONSTELLATIONS CARRIER PHASE AND PSEUDORANGES
GPS, Galileo, Other For High Precision
CORRECTION PARAMETERS
NETWORK CONTROL CENTER
For each satellite
Compute corrections
INTERNET
USER GNSS RECEIVER
REFERENCE STATIONS
With PPP Support
A. UPLINK STATION
To GEO Satellite
Figure 4: Precise Point Positioning (PPP)
In order to deal with local biases, such as atmospheric conditions, multi-path and satellite
geometry, a convergence time is required to achieve decimetre level or better accuracy (typically
up to 3 cm). To obtain a 10 cm horizontal error, a convergence time between 20 and 40 minutes
is usually necessary. Convergence time depends on the number of satellites available, satellite
geometry, quality of the correction products, receiver multi-path environment and atmospheric
conditions.
When comparing PPP with differential processing, the main disadvantage of PPP is that
usually it takes longer to converge [15], due to the lack of ionosphere and troposphere informa-
tion. On the other hand, the differential RTK solution performance degrades with the increase
of distance between the rover and the reference station.
PPP-RTK can be seen as an extension of NRTK with SSR, or also as PPP with fast ambiguity
resolution [15]. In addition to the orbits and clocks, information about the satellite phase biases
is also sent to users [17], reducing the convergence times when compared to PPP-AR [18].
PPP-RTK provides all state parameters that are relevant for centimetre accuracy, including
for ionosphere and troposphere using SSR messages, that can be directly used by the rover to
correct his own observations [17]. SSR has good scalability compared with OSR, and in terms
of performance, SSR local reference station effects are greatly reduced or eliminated. Regional
services in Korea and Japan provide SSR data for free, based on the GNSMART software from
Geo++ [17].
3.3. Global Correction Services
Figure 5 places the main GNSS augmentation techniques in terms of accuracy versus coverage.
Although some augmentation techniques provide a good accuracy, the technical requirements
(required base stations, coverage) may not be suitable to evaluate a positioning system for AD
on a global scale.
ACCURACY
< 5 cm
ACCURACY
RTK PPP (PP)
5 cm
WARTK
CARRIER PHASE
10 cm PPP (RT)
20 cm
DGNSS
PSEUDORANGES
50 cm GBAS
SMOOTHED
1m
SBAS
3m
PSEUDORANGES
GALILEO, GPS III
10 m
GPS, GLONASS
20 KM 50 KM 500 KM BASELINE
WORLDWIDE
Based on: https://gssc.esa.int/navipedia/index.php/GNSS_Augmentation
Figure 5: GNSS Augmentation
There are several providers of global PPP correction services, with products where the error
and the convergence time vary (see Table 1) [19, 20]. Almost all of them charge a fee to access
the corrections. The announced performance is usually measured in static conditions over long
periods of time [19].
Table 1
Correction Services Comparison
HORIZ. RMS
SERVICE CONDITIONS CONV. TIME SUPPORT SOURCE
ERROR (cm)
TerraStar-C 3.3 - 5.3 Static 30 min GPS/GLO [20] , novatel.com
TerraStar-C PRO 2.5 Static < 18 min GPS/GLO/GAL/BDS novatel.com
TerraStar-D 4.1 - 5.9 Static * GPS/GLO [19]
TerraStar-X2 2 * < 1 min GPS/GLO novatel.com
OmniSTAR G2 4.4 Static < 45 min GPS/GLO [19] , omnistar.com
IGS (Final)1 2.9 - 5.6 Static 12 - 18 days GPS/GLO [19] , igs.org
VERIPOS Apex <5 Static * GPS/GLO/GAL/BDS veripos.com
StarFire <5 * * GPS/* navcomtech.com
* - No information or unclear. 1 - Free. Remaining are commercial. 2 - Regional coverage. Remaining are global.
Post-Processing techniques can also be used to obtain the maximum accuracy for applications
that do not require real-time positioning. In post-processing, data can be processed offline using
forwards and backwards smoothing, allowing to minimize errors that would be obtained in real
time [21].
4. Fusion of GNSS with Motion Sensors
A reference system can fuse GNSS signals with information from other sensors [22], such as
Inertial Navigation Systems (INS) and Distance Measuring Instruments (DMI). While an INS,
due to integration drift (very significant in lower grade INS), provides an accurate relative
measure of position only in the short term, GNSS provides an absolute position in the long
term. The integration of INS and DMI technologies allow to complement irregularities in the
GNSS with continuous inertial, speed and distance measurements, improving the quality of
the ground truth data, even in GNSS signal outages or when the line of sight to satellites is
blocked.
An INS uses an Inertial Measurement Unit (IMU) to obtain angular velocity and linear
acceleration measurements. These are used to compute a relative position and orientation
(roll, pitch and heading) of the system over time in relation to a starting point, by applying
dead reckoning techniques. There are different IMU grades, usually divided in: marine, aviation
(sometimes grouped as navigation grade), tactical and consumer. Each grade has different bias
[23], with higher grades translating into lower accumulated errors.
IMUs can use MicroElectroMechanical System (MEMS) accelerometers and gyroscopes, or
higher quality sensors such as Servo accelerometers and Fiber-Optic (FOG) or Ring Laser
(RLG) gyroscopes. FOG and RLG do not contain moving parts, therefore they generally
perform much better over vibration and shock. More information about FOG and MEMS
gyroscopes can be found in [24, 25].
The gyroscope’s bias is a critical point, since an error in the orientation will translate to an
integration of part of the acceleration from gravity (which is usually much greater than the
linear accelerations from the vehicle itself) in a different direction, leading to drift that, if not
compensated, increases exponentially with time [26].
The integration of a DMI into the reference system provides speed and distance information
that can be used to reduce the error accumulation of the double integration process. It can
also be used to detect when the vehicle is immobile, allowing some IMUs to self-calibrate, as
well as for integrity, complementing GNSS Receiver Autonomous Integrity Monitoring (RAIM)
techniques, which are based on a consistency check of satellite measurements [27]. The integrity
requirements for AD are very strict, due to the small safety distances that autonomous vehicles
are required to handle [5]. Integrity is also an important parameter for a reference system,
since the ground truth data must be reliable to characterize accurately the GNSS system being
evaluated.
Wheel-mounted rotary shaft encoders and non-contact optical sensors are examples of DMIs
that can be installed in a vehicle and used in a reference system. Wheel-mounted devices are
affected by measurement errors (≈ 0.12 km/h [28]). Wheel slipping due to loss of traction,
wheel lifting above the ground (e.g. during tight curves or inclined pavements) and tire wear
also introduce errors. Non-contact optical sensors provide slip-free measurement of distance,
speed or angle and some models can be used at high speeds (up to 400 km/h). They are
widely used in vehicles to evaluate parameters such as braking systems, tyres and sideslip
angles [29, 30] and in demanding fields, (e.g. in Formula 1). The downside is the cost of this
type of device (≈ 30Ke), when compared with the wheel-mounted option (≈ 5Ke).
There are devices that integrate a GNSS receiver and an INS device, some into a single
enclosure. In addition, many of these devices allow the input from a DMI. These integrated
devices are used in different applications (e.g. mapping and surveying) and the cost of a high-
end system is virtually unlimited. Many of them can be configured with the state of the art
technologies, including high end IMUs, with Servo accelerometers and FOG or RLG.
Depending on the level of integration (GNSS+INS), the device architecture can use loose,
tight or deep coupling (Fig. 6). The typical approach used for sensor fusion is Kalman filtering,
with: loosely-coupled, the sensor fusion is performed at the solution level (high grade INS are
required); tightly-coupled, the sensor fusion is performed at the measurement level (requires
more processing power); deeply-coupled, the sensor fusion is performed at the signal processing
level (requires feedback to the GNSS measurement engine).
POSITION, VELOCITY
POSITION, VELOCITY, ANGULAR RATE AND
LOOSE COUPLING
TIGHT COUPLING
ORIENTATION ACCELERATION
INS POSITION IMU POSITION
VELOCITY VELOCITY
TIME TIME
POSITION, VELOCITY, KALMAN FILTER CODE, PHASE, TIME, KALMAN FILTER
TIME DOPPLER
GNSS GNSS
POSITION, VELOCITY
ANGULAR RATE AND
DEEP COUPLING
ACCELERATION
POSITION
IMU VELOCITY
TIME
CODE, PHASE, TIME, KALMAN FILTER
DOPPLER
POSITION, VELOCITY
GNSS
Figure 6: GNSS+INS Integration Architecture
The benefits of tightly coupled systems are presented in [26], using real-world aircraft and
land vehicle datasets. The authors show that a tightly coupled system provides a distinct
advantage in urban environments, maximizing the amount of GPS measurements available for
aiding in real-time and post-processing. The deep coupling approach uses feedback to the IMU
and GNSS receiver, which improves the cold start and the reacquisition time, however most
GNSS+INS devices on the market are loose or tight coupling.
Some GNSS+INS devices support multi-constellation and dual antenna, that can be installed
in the vehicle (e.g. two meters apart), providing heading estimations from GNSS signals
[22, 31, 32], with an accuracy proportional to the distance between antennas. These estimations
are a complementing source of heading information, since the use of magnetometers inside of a
moving car is not possible due to the harsh magnetic environment. In addition, they can also
be used for integrity and to reduce the drift, when the vehicle is immobile or moving at low
speeds.
5. Global Reference System Architecture Proposal
Considering the benefits and limitations of the technologies, services, and approaches discussed
above, the architecture presented in Figure 7 was defined in order to meet the requirements
presented at the beginning of this paper. The proposed reference system solution is based
on a Tightly-Coupled GNSS+INS device, with dual antenna, and an Optical DMI or/and
a Wheel DMI to provide velocity information. Another essential element of the reference
system is the GNSS correction service. As stated before, reliable centimeter-level accuracy is
only obtained with GNSS correction data and post-processing. Although a camera does not
provide information that can enhance the performance of the reference system, it is essential,
for example, to identify possible sources of perturbation on the data from the other sensors.
d. PPP CORRECTION SERVICE b. DUAL GNSS ANTENNA
Correction Parameters GNSS Signals/ Heading Estimation
e. CAMERA
Route Conditions Recording
c1. OPTICAL DMI
Speed and Distance a. GNSS+INS (Tightly-Coupled)
Absolute and Relative Position
Orientation
c2. WHEEL DMI
Speed and Distance
Figure 7: Architecture of the Proposed Global Reference System for Automotive
The performance of the reference system is directly linked to the selected GNSS receiver
and IMU. However, there are hundreds of these devices on the market, and selecting the best
combination is a challenging task. There are several GNSS+INS as well as DMI devices on the
market with different characteristics and cost. The technologies in these devices are usually
protected, therefore the information regarding the devices’ characteristics and operation is
very limited. Gathering information such as the one presented in Table 2 is difficult, because
data sheets do not fully specify the conditions under which the tests were performed, making
the comparison impossible or unfair. As we can see in Table 2, GNSS+INS devices with very
distinct characteristics and price range (≈ 20 - 100+Ke) announce similar positioning and
orientation performance.
Considering the information available from the manufacturers and presented in Table 2, all
these devices report horizontal accuracy of 2 cm after applying DMI information, correction
data, and post-processing. However, as mentioned earlier, these performances are usually
obtained for best-case scenarios (e.g., open sky conditions), leading to different performance
in real-world conditions. Moreover, the high price tag of these devices, limits the access to
compare multiples devices in fair experiments with the same conditions.
Without reliable information, we opt to base the selection criteria on the characteristics and
limitations of the technologies, and not on announced performances. This is the reason why
a systematic study, such as the one presented in this paper, is important. In this context,
considering these aspects, a device with Servo accelerometer, RLG or FOG, dual antenna, and
DMI support, is one of the best candidates to obtain a stable 2 cm accuracy in real-world
conditions.
Table 2
GNSS+INS Devices Specifications Comparison
BIAS STABILITY DUAL DMI HEADING (deg) ROLL&PITCH (deg) HORIZ. POS. INS+RTK HORIZ. POS.
DEVICE ACC GYRO COUPLING
ACC (mg) / GYRO (º/hr) ANTEN. IN. GNSS / GNSS OUTAGE GNSS / GNSS OUTAGE (GNSS / GNSS OUTAGE) m (POST-PROC.) m
NovAtel SPAN CPT7 MEMS MEMS 1.7 - 3 / 0.25 - 0.45 (1σ) Yes Tight * 0.03 / 0.04 (10s) 0.01 / 0.02 (10s) 0.02 / 0.12 (10s) 0.01
Honeywell Hguide n580 MEMS MEMS 1.7 - 3 / 0.25 - 0.45 (1σ) Yes * * 0.05 (1σ) / 0.07 (10s) 0.015 (1σ) / * 0.01 (1σ) / 0.2 (10s) *
Trimble BX9404 MEMS MEMS * No Tight * 0.50 / 0.50 (10s) 0.10 / 0.10 (10s) 0.05 / 0.3 (10s) <0.04
Trimble BX9924 MEMS MEMS * Yes Tight * 0.09 / 0.50 (10s) 0.10 / 0.10 (10s) 0.05 / 0.3 (10s) <0.04
Applanix POS LV 6204 * * * Yes * Yes 0.02 / 0.02 (60s) 0.0051 / 0.0051 (60s) 0.0351 / 0.0351 (60s) 0.020
Applanix POS LVX MEMS MEMS * Yes * Yes 0.09 / 0.30 (60s) 0.031 / 0.091 (60s) 0.021 / 11 (60s) *
OxTS RT3003G Servo MEMS 0.002 / 2 Yes Tight Yes 0.1 (1σ) / * 0.03 (1σ) / * 0.01 / * *
SBG Ekinox-D MEMS MEMS 0.002 - 0.005 / < 0.5 Yes * Yes 0.05 - 0.08 / * 0.02 - 0.03 / * 0.01 / 31 (60s) 0.02
SBG Apogee Land/Air MEMS MEMS < 0.015 / < 0.08 Yes * Yes 0.04 / 0.06 (60s) 0.008 / 0.012 (60s) 0.011 / 0.51 (60s) < 0.011
iXblue Atlans * FOG * / 0.1 No SIGIL3 Yes 0.02 / 0.025 (60s) 0.008 / 0.008 (60s) 0.006 / 0.35 (60s) 0.006
iMAR iTraceRT-MVT-510 * FOG 0.015 / 0.01 Yes Tight Yes < 0.01 / 0.02 (60s); 0.28 sec(lat)2 < 0.01 / < 0.01° (60s) 0.02 / 0.1 (10s); 0.31 (60s) 0.02
iMAR iTraceRT-MVT-600 Servo RLG < 0.012 / < 0.0015 Yes Tight or Loose Yes < 0.01 / 0.086 sec(lat)2 <0.01 / < 0.025° 0.01 / 0.05 (10s) 0.02
Note: These specifications must be used only as a reference. The official data sheets must be * - No information or unclear. 2 - Gyro-compassing, no GNSS. 4 - Unclear ITAR restrictions. The remaining are ITAR free.
consulted before acquisition. 1 - With DMI. 3 - SIGIL: Septentrio iXblue GNSS Inertial link.
As mentioned before, the correction service is also one of most important parts to achieve
high accuracy on a global scale, and from the announced performances (usually also for op-
timistic scenarios) (see Tab. 1), TerraStar is one of the services with higher performance.
However, it is also important to consider that some of the GNSS+INS devices only work with
a limited set of correction services or even with only a single proprietary one. Therefore, the
correction service must be selected considering the GNSS+INS device.
Other practical aspects must also be considered when choosing a GNSS+INS device, such
as the fact that some of these devices may not be ITAR free (US International Traffic in Arms
Regulations), and these restrictions can lead to shipment delays or even in limitations of use
in some locations.
6. Conclusion and Future Work
In this paper, the challenges related to the characterization and evaluation of GNSS systems
for precise automotive positioning, in real-world driving scenarios, were discussed, resulting
in an architecture that is proposed as adequate for an Automotive Global Reference System.
Several technologies must be combined to create a reference system able to obtain precise
ground-truth. High-grade dual antenna, multi-constellation GNSS+INS devices (with Servo
accelerometer and RLG), as well as an optical DMI device, ensure the best available technology
for this type of reference system. However, the high cost is a limitation, when considering
worldwide tests with multiple vehicles. Correction services were discussed since they play a
major role in achieving centimetre-level accuracy on a global scale. The specifications of most
of these services show that in post-processing, it is possible to obtain consistent and accurate
positioning. Therefore, since real-time evaluation is not usually required in the discussed
context, and RTK is not practical in urban environments and for worldwide testing, the use
of post-processing techniques is the ideal approach.
One of the main challenges in designing a reference system solution is that the perfor-
mance promoted by the manufacturers of GNSS+INS systems is very similar, despite very
distinct technologies and cost. The lack of real-world experiments conducted by independent
researchers makes it difficult to find the ideal cost/performance balance. Therefore, a future
work goal is to test different GNSS+INS systems in the same real world driving conditions and
compare the obtained results.
Acknowledgments
This work has been supported by: European Structural and Investment Funds in the FEDER
component, through the Operational Competitiveness and Internationalization Programme
(COMPETE 2020) [Project no 037902; Funding Reference: POCI-01-0247-FEDER-037902].
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