enc 2015 differential gnss+ins for land vehicule autonomous

DIFFERENTIAL GNSS+INS FOR LAND VEHICULE AUTONOMOUS NAVIGATION
QUALIFICATION
Gilles Boime(1), Emmanuel Sicsik-Paré(1), John Fischer(2)
(1)
Spectracom,
Les Ulis, France
[email protected]
[email protected]
(2)
Spectracom
Rochester N.Y., USA
[email protected]
Abstract
Navigation technologies rapid improvement enables to integrate for land vehicle Advanced Driver Assistance System
(ADAS). Next step is to switch to fully autonomous car and Autonomous Land Vehicle (ALV). Integration of such
vehicles into the public road traffic needs to raise level of confidence and reliability of the automated navigation
guidance system. Automotive system integrators and car manufacturers shall enhance qualification of their systems
integrated in the platform with all sensors. Scenarios shall be reproduced to testify the accuracy and accountability of
the solution.
Specific qualification tools shall be used in order to qualify the navigation systems with relevant margin. It needs to
provide centimeters range accurate position with and without GNSS solution. Environment stress shall not corrupt
accuracy. Environment for road trip is much demanding. It includes urban canyon, multipath, foliage attenuation, tunnel
or bridge obscuration and antenna on the mobile temporary attenuation. It shall also resist to GNSS signal corruption.
Such tool has been developed from high end defense application with centimeter level accuracy. Technologies derived
from advanced military development are now accessible and affordable for civil use. Real-time differential correction
using fast Epoch-by-Epoch™ computation of the GNSS dual frequency solution enables to stick to large dynamics. We
demonstrate with a couple of equipment, how accurate real-time navigation of a rover can be. One unit is used as a
fixed reference GNSS receiver nearby the trial area. The other one is embedded in the rover to be tracked. The GNSS
receiver on the rover is using error computed by the fixed one to compute differential error compensation on the
satellite solution. The tight coupling of the Fiber Optic Gyroscope (FOG) inertial navigation system with a GNSS
receiver and the fast update (10Hz) rate of position deliver an all conditions with no interrupt solution. Practical tests of
the application on specific trials are performed and computed to derive position error. Error is analyzed to separate
systematic and random parts. Horizontal Dilution Of Precision (HDOP) of the GNSS solution is used to compare tests
samples.
Results are showing reliability and repeatability of the positioning solution that fit with the calibration error expected
for safety critical navigation function. It enables to automate and reproduce qualification tests. It fulfills a mandatory
step to go to the market for autonomous navigation products breaking one technical barrier to catch confidence for
automotive operator.
1. STEPS TO THE DRIVERLESS VEHICLE
Personal cars and professional trucks are continuously improving the driver experience and safety thanks to integration
of more significant and machine assisted control systems. Advanced Driver Assistance System (ADAS) are now
integrated in all luxury cars and progressing at mainstream products. Technologies covered by ADAS at specials for
each car integrator. It begins with breaking assistance and anti-slipping systems.
But ADAS is now taking care of more safety features, driver assistance and partial delegation to autonomous control for
small part of maneuvre. The generation introduced in early 2015 on highend models are engaging more intelligence
from the control system such as:
- Lane departure warning system
- Speed assistance and control
ENC 2015
- Driver assistance and control
- Autonomous emergency breaking
And it is not only individuals care and satisfaction that track the technology, but also the states level involvment to
prevent accidents and minimize the economic impact associated with. In European Union, the general safety regulation
2009/661 was the first step to engage member States to act as a regulator to mandate car to improve. European
Transport Safety Council (ETSC, a non profit private association) „Revision of the general safety regulation 2009/661“,
Position paper relaesed March 2015. It promotes introductions of life saving technologies like Intelligent Speed
Assistance (ISA), Autonomous Emergency technology including all speed and pedestrian detection, lane departure
warning system in next step of regulation.
Car manufacturer are not behind. They understand thier customer expectation to minimize risk and experiment cool
trips. It is also a path to turn single vehicule to a full intelligent, connected and entertainment object with high value
associated. So, every car manufacturer is willing to be seen as a technology master. Not the least, Toyota claims to
include collision prevention technology to be integrated in all mainstream and luxury cars by 2017. The ADAS new
generation focuses on radar activated cruise control technology for collision prevention system. The control system is
maintaining distance with vehicule ahead and can stop the car if driver doesn't react. Next step on track is to monitor
driver attention with sensors like camera focussing on driver eyes and pressure of hand on the steering whell.
Fig. 1. Toyota test car for next generation ADAS
Fig. 2. Japan Prime Minister Shinzo Abe riding in an
autonomous drive car Nissan Leaf
Collision prevention technology to be integrated in all mainstream and luxury cars from Toyota by 2017. This next
generation will focus on radar activated cruise control technology for collision prevention system. The control system is
maintaining distance with vehicule ahead and can stop the car if driver doesn't react. Next step on track is to monitor
driver attention with sensors like camera focussing on driver eyes and pressure of hand on the steering whell. No fully
driverless car expected by next 10 years. This technology is limited by legal issues and nation wide maping data
reliability.
Since technology shall be fully proven to prevent any lethal threat on user’s and road commuters, most of the car and
trucks companies are working actively on the targeted final step. It is said driverless vehicle. As an emblematic leader
Nissan is paving the way to such expectation. It began now with driver assist technology tested on open road traffic in
Japan since end 2013. It enables highly advanced system such as lane keeping, automatic lane change, automatic exit,
automatic overtaking of slower or stopped vehicule, automatic deceleration behing congestion on freeways, automatic
stopping at red light. It is a step to full automatic drive for 2020 targetted by Nissan.
Some in Europe are early trainers like Daimler Benz. They are using Bertha Benz prototype car to test autonomous
driving technologies. It merges multiple vision, radar, and GPS sensor with digital map to monitor a open road 100km
trip in August 2013 [1].
All are including driverless capability into their technology demonstration concept cars:
- Mercedes with F 015 Luxury presented at Consumer Electronic Show (CES) early 2015
- Audi with Prologue, an extrapolation of test car RS7 concept equiped with SuperFast driverless pilot
- BMW electric i3 car is integrating ActiveAssist technology that enable portions of drive itself without any manual
command such as car parking and autonomous rally to a meeting point.
- Google self driving vehicule that conforms to California license requirements for driverless tests in open traffic
- Testa model SD autonomous test car.
ENC 2015
Fig. 3. Bertha Benz test car running fully autonomous 103 km trip in open road including 27% urban narrow road
Despite the fact that most of the established market leaders are agreeing that it is not a technology for the mainstream
production for next few years, they all work very efficiently to master the technologies. But it is a big challenge to
integrates all the sensors and the navigation functions to position autonomously the vehicle on a map that shall be
accounted for. The whole system shall be cirtified to prevent any liability in case of dramatic crash that can engage the
solution provider and the vehicle manufacturer.
A large part of the job shall benefit of simulations and integration platforms in realistic conditions. But at least, a very
robust final open space validation shall take place. Car manufacturers / integrators are using private test facilities in
open air to perform serious trials before stepping to the traffic. Renault is using a 10 km2 facility in France to perform
private test in a protected area.
0.5 km
Fig. 4. Renault test center in open air at Aubevoye, France
But the new autonomous drive car test mandate to introduce equipment enabling measurement of the car position on the
track with a extremely high precision and repetability. There are two competing technologies to do it:
- Include many neigbour location sensors on the test track
- Use a general absolute positioning system
Here we focus on the absolute positioning system that is affordable, easy to install and low maintenance.
It is based on two main assertions:
- The autonomous pilot can position accuratly on the test track
- The test track is accuratly reference to the absolute positioning system
We are again focusing on the first assertion, the second one can be covered with a specific calibration trial where
equipment such as discussed further can be used in quasi-static mode and experiment consistent accuracy. Let us have a
deeper look to candidate position technologies to verify the autonomous pilot accuracy.
ENC 2015
2. VEHICLE POSITIONING CANDIDATE TECHNOLOGIES
There have been many proposed technologies to get position of the vehicle on the trip. But all shall be compatible with
a reliable mapping database. In the lack of consistent equipment of road infrastructure with alternative capabilities,
Global Navigation Sattellite Systems (GNSS) positioning is the sole enabling to fit to a map every place over the earth.
That is why driverless systems are always including a GNSS sensor to help other data matching with the map.
Versatility and low cost of GNSS positioning makes it a candidate for open air validation as well.
2.1. Standalone Standard Positioning Service GPS
The SPS single frequency GPS receivers are included in so many nomadic appliance that they feel like a commodity.
Since their introduction 20 years ago the performances are well enderstood. Results are all consitent with [2]. Some
trials were performed in different area profiles with satellite constellation Position Dilution of Precision (PDOP) < 2.
Worse results are from deep urban cannyon in downtown area Seattle WA.
For every technology the performances relevant for the road trip is the lateral error to the expected center of the lane in
the two horizontal dimensions refer to as 2D or N/E for orientation North and West.
It appears a lateral error standard deviation in 2D raising to 46 m and peak error up to 660 m.
Lateral error in 3D is raising to 20 m and peak error up to 175 m.
Such performances are out of range for any positioning verification. It can only deliver a rough estimate of the area on
the map that can be worth with tigh correlation with other sensors for the navigation system.
2.2. Hybridized IMU and SPS GPS
Coupling of absolute navigation GPS receiver with a IMU can mitigate the navigation solution corruption when
intermitent GPS signal corruption are faced. It is profitable on any difficult signal transmission from satellite through
line of sight. It happens in urban cannyon, deep folliage, under-bridges, tunnel and any multipath area. It also benefit to
the very short term (less then a few seconds) dispersion on the position computed from the sky.
Over last 10 Years, combined benefit of MicroElectroMechanical Sensor (MEMS) and tigh coupling algoritm raises the
bar of positioning achievement. It enables smoothed position along track and dead reckoning (DR) in case of GNSS
signal outage. Consistent results has been demonstrated from different tests as described in [3], [4], [5].
Lateral error standard deviation in 2D is lowered to 2.3 m and peak error up to 10 m.
Fig. 5. Comparison of RMS error from standalone GPS to tightly coupled MEMS/GPS SPS with adapted Kalman
filtering
Performances are already to weak to validate a vehicle position on the lane.
ENC 2015
2.3. Hybrid Differential Single Frequency and IMU
Next step in the global navigation in order to mitigate systematic errors delivered by the space and ground control
segment of the GNSS is to use a single of multiple reference receivers in the vicinity of the area covering the trip. The
reference receiver shall be static. The position of the reference is using long term averages to mitigate constellation
errors. A minimum for position fix of 20 min. Is commonly reported. Then the position error standard deviation in 2D is
less than 2 cm for baselines shorter than 100 km.
For a MEMS integrated with a standard SPS GPS single frequency receiver with DGPS correction on a mobile less than
70 km/h speed, with HDOP < 1.4, next table shows compared performances from [6].
Position error (m)
2D (N/E) standard dev.
Bias (N/E)
Standalone
2.2
4.4
Differential C/A
0.9
0.2
IKF
0.21
0.49
Fig. 6. Cumulative probability functions of single point error
Performances are interesting but still not enough accurate to validate autonomous car navigation.
2.4. Hybrid Differential Dual Frequency Carrier Phase and IMU
GNSS solution can again be improved taking into benefit both L1 and L2 frequencies to mitigate propagation error and
carrier phase to achieve ultimate signal capability. Combination of both help solving ambiguities associated with the
carrier phase technique. Then combined with MEMS IMU, accuracy confirmed within [7] with HDOP < 1.6 is:
Lateral error standard deviation is down to 0.18 m,
Peak error is 0.6 m.
It is still a limited accuracy when compared to 0.1 m to verify.
With such low cost IMU, GPS outage is showing a rapid increasing lateral error with elapsed time. The lower the speed
the poorer the position comes. When using an IMU with bias < ±0,75 °/s, random walk < 4.5 °/h0.5
When speed of the mobile is 15 m/s (54 km/h), position solution error in DR increasing pace is fast.
Fig. 7. Increase in lateral error as a function of outage elapsed time.
ENC 2015
Another limitation common to many differential solution is the turn on delay for the solution. It is also a repetitive issue
in case of disruption of the GNSS solution. It widen the delay to recover from DR status.
3. HYBRIDIZED DUAL FREQUENCY DIFFERENTIAL GPS WITH PERFORMANT IMU
3.1. Benefits of Dual Frequency Differential EBE
High skills navigation experts develops a has developed a new class of instantaneous, real-time precise GPS positioning
and navigation algorithms, referred to as Epoch-by-Epoch™ (EBE) [8], [9].
Compared to conventional Real-Time Kinematic (RTK), integer-cycle phase ambiguities are independently estimated
for each and every observation epoch. Therefore, complications due to cycle-slips, receiver loss of lock, power and
communications outages, and constellation changes are minimized. There is no need for the initialization period
(several seconds to several minutes) required by conventional RTK methods.
More importantly there is no need for re-initialization immediately following loss-of-lock problems such as occurs
when a mobile GPS receiver passes under a bridge or other obstruction, or loses satellite visibility during a shaded
portion of road. In addition, EBE provides precise positioning estimates over longer reference receiver-to-user receiver
baselines than conventional RTK.
This feature supports testing for long-range operations, for example, positioning vehicle on a lane. The reference
receiver is set on the vicinity of the test center track.
EBE requires the use of a minimum of two receivers, each of which is tracking a common set of five or more satellites
and providing simultaneous dual frequency phase data. One of the receivers is stationary but this is not a requirement.
EBE has been proven utilizing dual frequency receivers and operating at distances of up to 50 km from the nearest base
station in unaided mode. Additionally the EBE algorithms operate in a network environment and make optimal use of
all GPS measurement data at each epoch, gracefully degrading the position accuracies when some measurement data are
not available. Further, the system will make use of IMU system, compensating for outages when sight to the satellites is
blocked. This results in a robust and more reliable system.
Epoch-by-Epoch™ promises numerous benefits including:
• Computationally efficient algorithms that provide a position estimate based on a single epoch in several milliseconds.
This allows the real-time position estimate to be computed on the user platform (assuming reference station data is sent
to the user platform).
• An initialization period is not required. Since RTK requires some period of time (that can be measured in seconds to
minutes) to perform ambiguity resolution, this is an important capability for
platforms that: Require high accuracy (e.g., for end game scoring); cannot see the satellites until launch; and have short
flight duration.
• A reinitialization period following loss-of-lock is not required, unlike RTK, which needs to restart the integer-cycle
phase ambiguity resolution process. This is another important capability because vehicle monitoring is considering EBE
for dynamic applications where loss of lock and loss of data are likely.
However, it must be mentioned that many of the GPS receivers in use by the test (and training) community today do not
support this dual frequency requirement. Hence, those systems could not realize the maximum benefit.
This technology is implemented in a rugged modular platform Geo-iNAV with 3 main units:
A dual frequency GPS antenna
A integrated INS coupling GPS and IMU and integrating performant MEMS IMU
An external IMU for high end accuracy and reliability, Fiber-Optic Gyroscope (FOG)
The external IMU is optional and dedicated to increase capability of DR.
Table 1. IMU performances grades
Geo-iNav
Tactical (internal
MEMS Epson MG362)
Advanced (external
FOG KVH 1750)
ENC 2015
Parameter
Measurement range
Biais (in-run) stability
Random walk
Measurement range
Biais (in-run) stability
Random walk
Accelerometer
±3 g
< 0.1 mg
0.04 m/s/h0.5
±10 g
7.5 mg
0.07 m/s/h0.5
Gyroscope
±150 °/s
3 °/h
0.2 °/h0.5
±490 °/s
0.05 °/h
< 0.012 °/h0.5
External
IMU
Geo-iNAV
External IMU
Cable
GPS
Antenna
Ethernet
Cable
Power Cable
GPS Antenna Cable
Fig. 8. Dual Frequency differential navigation unit hybridized with external FOG
3.2. Performances
Test has been performed in conditions close to the land vehicule navigation validation. It is based on measurements on
the fly with no post-processing on positioning except for evaluation of the error.
First a static position of the rover 4.8 km away from the reference receiver. Position are updated once per second.
System is including FOG IMU.
Lateral error peak is lower than 4 cm
Biais error is lower than 1 cm
See figure bellow
Fig. 9. Single point error when rover is static
Fig. 10. Dynamic trial test single point error
Second test is with high dynamic mobile, speed 200 km/h, average distance from the reference to the rover is 6 km
apart.
Lateral error standard deviation is 0.5 cm, peak error is lower than 2.2 cm
Biais error is lower than 0.2 cm
Then performances on the test are matching the expected accuracy for validation of autonomous navigation.
ENC 2015
Regarding the profile of trial to be performed switch can be done between IMU class of performance. When in line of
sight sky-view, performances are same level.
Table 2. Horizontal error performances
Geo-iNAV grade L1/L2
Tactical
Advanced
Horizontal Position Accuracy (RMS)
Autonomous
Differential
1.5 m
5 cm
1.0 m
5 cm
4. CONCLUSION
Real-time differential Epoch-by-Epoch dual frequency carrier phase GPS receiver tightly hybridized with a performant
IMU can provide absolute error lower than 5 cm in the 10 km range of the reference static receiver. This is fully adapted
to the qualification of driverless auto-pilot systems for the 2020 years. It can save complex theodolite and vision
calibration systems. It provides flexible and minimum sustaining.
5. ACKNOWLEDGEMENT
Present study has been solely possible thanks to the great materials provided by Geodetics Inc. and warm advises of Dr.
Jeffrey A. Fayman, Vice President of Geodetics Inc.
REFERENCES
[1] Making Bertha Drive – An Autonomous Journey on an Historic Route, IEEE Intelligent Transportation Systems
Magazine, vol. 6 pages 8-20 2014, J. Ziegler, P. Bender, M. Schreiber & al.
[2] Tracking Vehicle with GPS – Is it a Feasible Solution? 13th Annual Meeting of ITS America, Nov. 2002
Proceedings, Jianyanq Zheng, YinHai Wang, Nancy Nihan, Department of Civil and Environmental Engineering,
University of Washington, Seattle WA
[3] Improved Vehicle Positioning in Urban Environment through Integration of GPS and Low Cost Inertial Sensors, in
ENC 2009, P. Davidson, J. Hautamäki, Jussi Collin, Jarmo Takala
[4] Improving GPS-Based Vehicle Positioning for Intelligent Transportation Systems, IEEE Intelligent Vehicle
Symposium (IV) June 2014 Proceedings, pages 1023-1029, Arghavan Amini, Reza Monir Vaghefi, Jesus M. Dela
Garza, R. Michael Buherer, Virginia Tech.
[5] Cooperative Vehicle Position Estimation, in proceedings of ICC 2007, pages 5837-5842, R. Parker, S. Valaee.
[6] Differential GPS for small UAS using consumer-grade single-frequency receivers, International Micro Air Vehicle
and flight competition conference 2013, J. Bolting, F. Defaÿ, J-M. Moschetta
[7] Dual Frequency, Carrier Phase Differential GPS Augmentation, ITS Institute, center for Transportation Studies,
Final report, May 2011, Eddie Arpin, Bryan Newstrom, Craig Shankwitz
[8] Epoch-by-Epoch™ Positioning and Navigation, in Proceedings ION GPS-2000, The Institute of Navigation,
Alexandria, VA, pp. 337-342, P.J. de Jonge, Yehuda Bock, Michael Bevis, Geodetics Inc.
[9] Epoch-by-Epoch™ Real-Time Positioning in High Dynamics and at Extended Ranges, Proceedings of the ITEA
Symposium on Test & Evaluation of Advanced Technology Systems, Lihue, Kauai, HI, 2003, Yehuda Bock,
Thomas Macdonald, John H. Merts & al.
ENC 2015