“Seen & Heard” is a monthly feature of GPS World magazine, traveling the world to capture interesting and unusual news stories involving the GNSS/PNT industry.
Photo: Nuro
Hey, R2, Where’s my pizza?
Domino’s pizza will start using Nuro’s R2 unmanned vehicles for delivery in Houston, Texas, later this year. Once customers have opted in, they can track the R2 vehicle via the Domino’s app and will be provided with a unique PIN code to unlock a compartment to get their pizza. Nuro is already at work in Houston delivering goods from dinner to dry cleaning.
Screenshot: BBC
Drone Attack
A BBC documentary has sent the drone industry into a tizzy. “Britain’s Next Air Disaster? Drones” begins with the December 2018 Gatwick Airport incident when two drones entering airport airspace led to a disruption of operations for three days. Dronemakers dislike the documentary’s thrust that drones are a threat to public safety and a tool for terrorists, while barely mentioning their positive contributions in fields such as search and rescue, plant inspections and agriculture.
Photo: Rawpixel.com/Shutterstock.com
NavIC Rising
The Indian Space Research Organisation is in talks with chipmakers Qualcomm and Broadcom to substitute GPS in Indian mobile phones with its own satellite system (NavIC). The Times of India noted that cellphones hold the biggest commercial potential for NavIC, with more than 650 million mobile users in India. ISRO and the Indian Air Force are also working to equip fighter jets with the navigation system, and commercial vehicles registered after April 1 are mandated to have NavIC trackers.
Photo: Monitum Pty Ltd.
Infrastructure sensors are Mthing
Internet of things (IoT) project Mthing is researching GNSS monitoring sensors to record near-real-time measurements of infrastructure construction. The 18-month project in Brisbane, Australia, aims to develop GNSS IoT sensors that will provide cost-efficient, constant and high-precision monitoring that will connect to cloud services and provide instant alerts. Mthing aims to produce low-cost sensors with broad market potential. The research team includes Queensland University of Technology, survey company Monitum, and the Innovative Manufacturing Cooperative Research Centre.
Image-based positioning has not yet been certified in aviation applications. To cover numerous environmental conditions, the authors installed various optical sensors. They present an approach for fusing image data of two complementary cameras with different spectral ranges.
The use of two image sensors working in the visible light spectrum and infrared spectrum increases availability and accuracy, meeting requirements to be used as an augmentation for state-of-the art GNSS-based landing systems.
This investigation presents real flight data processed by means of the proposed method. This work constitutes a new approach for robust runway detection, since position calculation was only carried out once in one time epoch on a single blended image.
The proposed method was applied to data from two flight campaigns in post-process. A determined set of parameters lead to a sufficient level of availability and a valid runway detection throughout the final approach.
Citation
M. Angermann, S. Wolkow, A. Dekiert, U. Bestmann, P. Hecker (2018), “Linear blend: Data fusion in the image domain for image-based aircraft positioning during landing.” Pacific PNT Conference,www.ion.org/publications/browse.cfm
Aircraft navigation during landing approach is mostly supported by ground-based landing systems in commercial aviation, which cause high installation and maintenance costs.
Nevertheless, the final sequence of the flight before touchdown is mostly performed by the pilot manually, because of the high requirements for accuracy and integrity. Only a few landing systems can fulfill these requirements during the last 200 feet above ground.
The current work presents a further development of an optical positioning system to be deployed below 200 feet and on ground after touchdown in order to be used as an additional source for positioning information. The system is capable of visual 3D positioning of the aircraft relative to the runway.
Algorithms for threshold marking (see image below) and centerline detection, as well as lateral position calculation during rollout are presented. The system is evaluated during flight trials performed with the research aircraft Dornier Do 128-6.
Citation
S. Wolkow, M. Angermann, A. Dekiert, U. Bestmann (2018), “Model-based threshold and centerline detection for aircraft positioning during landing approach.” Pacific PNT Conference,www.ion.org/publications/browse.cfm
Terra Drone Brazil, a group company of Japan-based Terra Drone Corp., has successfully completed Brazil’s first drone inspection of an offshore FPSO tank. The unmanned FPSO tank inspection was undertaken for Brazil’s state-owned oil company Petrobras.
The ballast tank inspection using drones was conducted aboard P-66, a floating production, storage and offloading (FPSO) unit from Petrobras that is operating in the Pre Salt Area at Santos Basin. An FPSO is a floating vessel used by the offshore oil and gas industry for the production and processing of hydrocarbons, and for the storage of oil.
Petrobras needs its cargo and ballast tanks inspected regularly for maintenance. Any kind of corrosion, cracks, fractures or welding anomalies must be identified quickly before they can damage the structural integrity of the ship.
The drones are prepped for the tank inspection. The UAV inspection just over an hour. (Photo: Terra Drone)
Traditionally, this inspection is done by sending a team of up to four men inside the confined tank space using scaffolds or rope access. This kind of close-up visual inspection of one tank alone can take from half a day to a full day, and pose a safety threat to the workers inside the tank.
Using drones reduces the need for workers to enter the tank. “Not only is unmanned FPSO tank inspection safer, but it is also much quicker and more precise than manual inspection,” said Marcelo Belleti, executive director at Terra Drone Brazil. “Further, drone inspections for cargo tanks can lead to potential cost-savings as well.”
Terra Drone Brazil completed the inspection of a ballast tank for Petrobras in little over an hour with a team of only two men. The high-definition pictures and videos captured by the drone ensured a quality deliverable report for all 40 points pre-defined for the close-up inspection.
Terra Drone Brazil is certified by ABS (American Bureau of Shipping), DNV GL (Det Norske Veritas and Germanisher Lloyds) and Loyd’s Register as a service supplier approved for surveying using Remote Inspection Techniques (drones) as an alternative means for a close-up survey of the structure of ships and mobile offshore units. The Petrobras P-66 is ABS-certified.
Arvento’s imt.x1 has a six-axis gyro sensor that can sense three-dimensional movement caused by emergency acceleration, panic braking and directional yaw and drift.
With connectivity options including dual CANBus and Bluetooth, the system is also eCall compatible and captures and provides data for accident analysis and other vehicle tracking functions. The system also uses the next-generation powerful Arm-based microcontroller.
This latest launch is yet another product of a successful, eight-year strategic partnership between Arvento and u-blox. “U-blox is more than a supplier,” said Özer Hıncal, Arvento’s general manager. “As a global leader in the IoT [internet of things] industry providing high-performance IoT modules, platforms and support services, u-blox is our trusted solutions partner, working closely with us to address customer demands and issues.”
As for previous Arvento products, collaboration with u-blox was a key factor in the imt.x1 product development process. The system’s high position sensitivity and accuracy are based on integration of u-blox’s 2G, 4G and 5G-ready cellular modules as well as GNSS modules.
The development of the imt.x1 aligns with Arvento’s vision and mission as a developer of advanced fleet telematics and vehicle tracking devices and will be available from August 2019.
SiTime Corp. has unveiled its Endura micro-electro-mechanical system (MEMS) timing solutions for aerospace and defense applications including precision GNSS, as well as field and satellite communications, avionics and space.
The Endura products are engineered to provide high performance in harsh conditions — severe shock, vibration and extreme temperature — that are routinely experienced in these applications.
SiTime offers customers 5 million possible part numbers that can be created from 17 programmable products.
“When exposed to high levels of shock, vibration, and extreme temperatures, legacy timing components have been prone to failure, degrading system performance and reliability,” said Piyush Sevalia, executive vice president of marketing. “To solve these problems, SiTime created an oscillator system of silicon MEMS, analog circuits, compensation algorithms, and advanced packaging, which is designed to outperform any other available timing solution in harsh environments.
“For example, Endura precision TCXOs deliver 4 parts per trillion per g (ppt/g) of acceleration sensitivity, which is 50 times better than legacy quartz-based solutions. With such performance, we believe that Endura will transform the oscillator landscape in aerospace and defense.”
Highlights of the company’s solutions include:
4 parts per trillion per g force of acceleration (50 times better than quartz)
Supports –55 degreesCelsius and +125 degrees Celsius operation
Key timing specifications conform to MIL-PRF-55310
Five million possible part numbers
Endura Super-TCXOs (temperature compensated oscillators) for use in high-speed communications and GNSS applications include:
SiT5146/SiT5147 – 1 to 220 MHz, ±0.5 to ±2.5 ppm, -40 degrees Celsius to +105 degrees Celsius
SiT5346/SiT5347 – 1 to 220 MHz, precision ±0.1 to ±0.25 ppm, -40 degrees Celsius to +105 degrees Celsius
SiT5348/SiT5349 – 1 to 220 MHz, ultra-precision ±0.05 ppm
SiTime’s portfolio of commercial off-the-shelf (COTS) Endura products spans six oscillator types and 17 products. All devices offer programmable options such as frequency, operating voltage and stability.
In addition, some devices offer specialized programmable features such as spread spectrum, pull-range, and differential output type.
Endura products are available with up to two grades of acceleration sensitivity, as low as 4 ppt/g (typical). This breadth of products provides customers with a large selection and the ability to configure each device for their application requirements.
Endura products are also designed for continuity of supply for long-life programs.
What is the biggest safety challenge for autonomous vehicles?
John Fisher. (Photo: Orolia)
“Sharing the road with human drivers. Optimized safe driving algorithms are compromised to mesh with the human’s natural level of risk taking. But this reduces safety, delaying acceptance — a real conundrum. Now, if we could just eliminate the humans…” John Fischer
Orolia
Julian Thomas
“When AI systems can deal with 99.9% of situations, the challenge will be keeping the passenger engaged to take over quickly when the 0.1% happens. Imagine a truck in front with a load coming loose. Which one would you trust?” Julian Thomas
Racelogic
Members of the EAB
Tony Agresta Nearmap
Miguel Amor Hexagon Positioning Intelligence
Thibault Bonnevie SBG Systems
Alison Brown NAVSYS Corporation
Ismael Colomina GeoNumerics
Clem Driscoll C.J. Driscoll & Associates
John Fischer Orolia
Ellen Hall Spirent Federal Systems
Jules McNeff Overlook Systems Technologies, Inc.
Terry Moore University of Nottingham
Bradford W. Parkinson Stanford Center for Position, Navigation and Time
Our cover story this issue is all about autonomous vehicles. Retirees — not usually considered early adopters of technology — are trusting autonomous vehicles to ferry them from point to point using the technology our industry can offer.
We have also used a lot of magazine space to discuss unmanned aerial vehicles, or drones, and shown how they are taking on a lot of tasks formerly done by manned pilots or workers, such as aerial mapping or factory inspections.
So is the idea of an autonomous plane such a stretch?
At June’s Paris Air Show, Christian Scherer, chief commercial officer for Airbus, told the Associated Press that his company already has the technology to fly passenger planes without pilots.
Scherer also said in the AP interview that Airbus hopes to be selling hybrid or electric passenger jets by around 2035.
Airbus already has “the technology for autonomous flying.”
But having the tech is one thing. Winning over regulators and potential travelers is quite another.
“When can we introduce it in large commercial aircraft? That is a matter we are discussing with regulators and customers, but technology-wise, we don’t see a hurdle,” Scherer said.
In fact, in a new study, seven out of 10 people say they would be willing to travel in an unpiloted plane at some point in their lifetime. The survey was conducted by U.S. software firm Ansys, which is working to provide digital replicas of how planes and cars react in different situations.
Passengers would be more willing to embrace automation if firms could show that a computer would react in the best and quickest way if anything unexpected happens.
But are we there yet? Michael Wiggins, the chairman of the aeronautical science department at Embry-Riddle Aeronautical University in Florida, addressed the autonomous-flight adoption question for the New York Times.
“From what I see, could it happen in the distant future? I think it probably could. Will it happen in the near future? I don’t think so,” Wiggins said. “Right now, any progress toward that area should be done very slowly, very measured and only after a bunch of research with results that suggest we should do that.”
The sweeper Woxiaobai has been in service for a year. (Photo: Unicore)
Fall is a beautiful time of year. But when the leaves drop, it means a lot of sweeping for most of us. Not so for the 200 campuses and parks in China using IdriverPlus’ WO series of unmanned sweepers.
High-precision GNSS positioning plays an important role in making the autonomous units possible, providing real-time high-precision position, speed and time information.
The sweeper in Beijing’s Haidian Park. (Photo: Unicore)
Unicore’s high-precision GNSS technology and their products’ high reliability have enabled IdriverPlus’ unmanned sweepers and logistics vehicles — China’s first mass-produced products in intelligent driving. In January, IdriverPlus received the green light to test self-driving cars in Beijing.
Diagram: Unicore
Sweepers and logistics vehicles are not only used in open-sky areas, but also in complex environments shadowed by buildings or trees or experience multipath. These areas include school campuses, factory and science parks, and community squares.
Complex environments result in different GNSS availability, reliability and convergence. In autonomous driving, the inputs the vehicle receives from GNSS and other sensors must be accurate and reliable.
A customer removes her express package from the Wobida logistics vehicle. (Photo: Unicore)
The UM482 module used by the IdriverPlus is characterized by dual antennas, compact dimensions, high performance and low cost, providing anti-jamming performance.
Integrated with on-board MEMS and Unicore’s U-Fusion combination technology, the UM482 can effectively solve the disruption of positioning results caused by the loss of satellite signal, and further optimize the continuity and reliability of positioning and heading outputs in complex environments such as city canyons, buildings and tunnels.
Two companies have integrated GPS/PNT tech into a growing autonomous vehicle market: driverless shuttles for retirement communities. Powering the service, a cloud-based GNSS corrections system delivers centimeter-level accuracy without deploying and maintaining a GNSS network. This leading-edge application targets autonomy at scale and enables high-precision positioning for mass-market automotive and autonomous vehicle applications.
Photo: Voyage
For many seniors, retirement communities offer the best of both worlds: the freedom to live in their own homes and access to immediate assistance when they need it.
Driverless cars are an option several retirement communities have embraced to better serve residents who no longer have the ability or desire to drive, but want to retain the ability to come and go as they please.
“Autonomous vehicles are a great fit for any community where the environment is well-understood, less complex than dense urban areas, and the transportation demand is high,” said Justin Erlich, vice president of strategy, policy and legal for Palo Alto, California-based Voyage, a company that employs existing technology to develop fleets of autonomous vehicles. “Retirement communities satisfy all of these characteristics.”
Serving Seniors
Voyage deployed driverless shuttles to serve 130,000 retirees at The Villages, a massive retirement community encompassing more than 50 square miles in Sumter County, Florida.
“The community’s residents enjoy an extremely active lifestyle, but often face challenges getting around,” Erlich said. “Autonomous vehicles are perfectly suited to meet this demand.”
The six vehicles in the fleet stay within the confines of the retirement community, where all roads have been precisely mapped, speed limits are lower and traffic patterns are more clearly defined than in a typical city. The vehicles travel over a network of roads that span 750 miles.
THE VILLAGES
Location: Sumter County, Florida Area: 50 square miles Road span: 750 miles Number of retiree residents: More than 130,000 Number of Voyage autonomous vehicles: 6
To request one of Voyage’s autonomous vehicles, a resident can summon the shuttle on-demand with a smartphone. Voyage is working with residents on the possibility of using other shuttle-request options, including text messages, phone calls and well-marked pickup zones in crowded downtown areas, Erlich said.
All passengers ride with Voyage safety drivers in the front seat. The drivers take note of any “events” during rides so Voyage can investigate how to improve the riding experience.
Photo: Voyage
Eventually, residents will be the only passengers in the vehicles. If they need assistance during a ride, they will be able to communicate with remotely located Voyage employees, Erlich said.
Testing and rolling out fleets of driverless vehicles in private communities like The Villages allows Voyage to develop and perfect the autonomous vehicle technology it uses. As a result, the company can deliver the service to new clients in mere months.
Voyage, which has been working on its autonomous technology for more than two years, uses daily customer feedback to constantly adjust to its technologies to better serve riders.
“Feedback collected during test drives is one of the biggest factors in shaping our technology roadmap,” Erlich said. “Driving data — collected across all sensors and traffic scenarios — is automatically processed each night, highlighting interesting ‘events’ for our engineering team to analyze and review.”
During Voyage’s beta test process at The Villages, residents applied to be part of the company’s Pioneer Program for early access to the autonomous vehicles and the ability to offer feedback early on. Riders who test the service complete scorecards after each trip to help improve the experience for all riders.
Europe Takes the Lead
(Tire photo: iStock.com / TANAPHONG)
Autonomous vehicle technology is taking off in Europe, shows a study published by the European Patent Office and conducted with the European Council for Automotive Research & Development. From 2011 to 2017, European patent applications related to automated driving increased 20 times faster than other technologies in recent years. The “Patents and self-driving vehicles” study reveals automated driving patent applications at the European Patent Office rose 330%, compared with 16% for all technologies during the same time.
“As one of the only self-driving car companies that are picking up actual passengers as a part of our Pioneer Program, we believe we can learn a lot from the feedback we hear from our initial Pioneer riders as we try to make this the best service for The Villages,” said Oliver Cameron, co-founder and CEO of Voyage. “We are excited to see so much interest from other residents to become a part of this program.”
When developing autonomous technology, safety is Voyage’s top priority, Erlich said. Every change to the hardware and software used undergoes a multi-stage validation process. Company engineers perform “on-desk” tests of every change using unit tests, functional tests and a driving simulation environment. Then, an operations team runs suites of real-world traffic and validation tests in a completely controlled environment at a closed-course testing facility in San Jose, California
“Voyage makes extensive use of simulation testing and closed-course validation before any of our vehicles are driven in our partner communities,” Erlich said. “All changes must pass these closed-course tests before making their way onto the roads of our partner communities.”
Vehicle design also ensures riders stay safe. “Our fleet vehicles have been designed with multiple levels of safety redundancies for braking, steering and power, and leverage an advanced diagnostics system to automatically detect anomalies and safely stop the vehicle,” he explained. “In addition, we have developed a remote teleoperations solution that allows the vehicle to request additional help when a driver is not physically in the vehicle.”
Skylark provides high-precision localization. (Image: Swift Navigation)
Making Autonomous Work
When building an autonomous system, localization — knowing exactly where you are in the world — is critical. Erlich said it’s often difficult to estimate your position within an accuracy of several feet when using more traditional GPS solutions.
“For autonomous driving, you need to be able to estimate within several centimeters,” he added.
Voyage uses Swift Navigation’s GNSS receivers and Skylark network as one of the primary inputs into its localization solution.
Swift Navigation is a San Francisco-based tech firm that develops GPS technology to power autonomous vehicles. It is working to extend the Skylark network across the contiguous United States, and then plans to expand globally.
“Coupled with high-definition maps, odometry sensors and other inputs, we’ve been able to use Swift Navigation’s differential GPS solution to achieve the localization results we needed to deliver a true autonomous driving service,” Erlich said.
Voyage’s autonomous vehicles are equipped with a suite of sensors on their roof racks that includes the Swift Navigation Piksi Multi GNSS receiver, lidar devices, cameras, radar and an inertial measurement unit. They create and constantly update a 3D map of the vehicle’s surroundings.
Swift Navigation’s Duro is one of two GNSS receivers Voyage uses for its autonomous vehicles. (Photo: Swift Navigation)
A computer in the trunk integrates all sensor signals and uses the vehicle’s Controller Area Network (CAN) bus to operate steering, braking and other functions.
Skylark, Swift Navigation’s cloud-based GNSS corrections service, provides Voyage’s autonomous vehicles with precise positioning to eliminate the complexity of deploying and maintaining GNSS networks.
Skylark offers a plug-and-play experience that delivers convergence times measured in seconds. Its positioning algorithms provide a continuous data stream to individual devices from the cloud. This data stream allows for quick positioning and high reliability and availability.
The correction service enables receivers to connect to a constantly adapting, cloud-based model to obtain GNSS observations. Dependence on base stations in each area of deployment is eliminated, increasing the geographic area in which they can travel. Skylark works seamlessly with both of Swift Navigation’s GNSS receivers — Piksi Multi and Duro.
In addition to Piksi Multi and Duro, Voyage uses third-party receivers and microprocessors that benefit from the lane-level positioning Skylark delivers.
Equipment Specs
Photo: Swift Navigation
GNSS receiver one. Swift Navigation — Piksi Multi
• Dual-frequency and multi-constellation
• Up to 20-Hz solution rates
• Raw data outputs from on-board MEMS IMU GNSS receiver two. Swift Navigation — Duro
• IP67 rated
• Centimeter-level positioning
• Raw data outputs from on-board MEMS IMU Lidar devices. Velodyne — VLS-128
• 128 channels
• Up to 300-meter range
• Up to 360-degree surround view Cameras. iDS — Global-Shutter units Proximity sensors. Chrysler OEM Inertial measurement unit. Xsens — MTi-300
• 375-Hz bandwith for accelerometers
• 415-Hz bandwith for gyroscopes Antenna. Swift Navigation — Mini-survey for the Duro RTK unit
• 1 L1/L2 GPS/GLONASS/BeiDou mini-survey
The Swift product suite delivers centimeter-level localization —important to riders who may have mobility issues that require vehicles with smooth starts and stops.
Skylark was built specifically to deliver the speed, security, precision and reliability demanded by automotive manufacturers with autonomous and safety applications architected to support ASIL-rated (Automotive Safety Integrity Level) systems.
Because Skylark is a network, it is fault tolerant. In the unlikely event an individual cloud reference station goes offline, Skylark’s positioning algorithms will continue to provide a continuous stream of corrections.
Once connected, Skylark creates a precise and constantly adapting model of the atmosphere and related errors affecting GNSS. Connected users simply turn on their devices to get the precise positioning data they need.
Safety Drivers
As drivers get older, their mental and physical health can affect their ability to operate vehicles safely. Vision and hearing loss keep many older drivers off the road. Fear of driving at night or in the rain also can be a problem for older drivers. According to the Centers for Disease Control and Prevention (CDC), about 7,400 adults over the age of 65 died as a result of car accidents in 2016. That same year, more than 290,000 of adults over the age of 65 were treated in emergency departments for injuries sustained in motor vehicle accidents.
Residents at The Villages who have used the autonomous vehicles report positive feedback, Erlich said. They consider the service a major improvement to their day-to-day activities because it’s convenient. Plus, they prefer the ability to be more carefree during happy hour, fewer hassles with traffic and parking, and lack of interactions with poor drivers.
Being on the cutting-edge of a generational technology also is a positive for many residents, Erlich said. “Autonomous vehicles create a clear path to safer, more accessible, and reliable transportation for everyone. From a safety perspective, autonomous vehicles have the potential to significantly reduce the more than 37,000 deaths attributed each year to driving. From a lifestyle perspective, there are also huge opportunities: from reclaiming daily commute time, to providing a reliable means of transportation to people with mobility challenges.”
Positioning Intelligence Key to Autonomous
Hexagon’s Positioning Intelligence (PI) division is an integral partner in many autonomous vehicle development projects, providing technologies such as SPAN (GNSS+INS technology), TerraStar-X corrections, and Automated Research and Development Platforms from its brands including NovAtel, VERIPOS and AutonomouStuff.
NovAtel hardware and software products, along with engineering support, address the need for accurate, reliable and robust GNSS positioning. TerraStar-X correction services deliver worldwide coverage and assured positioning with continuous availability, and provide the accuracy and rapid convergence needed to achieve lane-level precision for safe autonomous operation.
For developers of autonomous consumer transportation, integrated research and development automotive platforms from AutonomouStuff accelerate time to market.
Making It Safe. For large-scale automotive production, safety is the main focus. The Hexagon PI software positioning engine and TerraStar-X technology are being developed to ASIL-B (Automotive Safety Integrity Level B) standards to provide precise positioning for lane-level performance in autonomous applications.
Image: Trimble
Road Corrections
Incorporating precise and consistent absolute location information is an essential component of enabling advanced driver assistance (ADAS) and autonomous driving (AD) technology for vehicles.
To help meet this need, Trimble recently released Trimble RTX Auto. The Trimble RTX Auto correction service provides a precise point position (PPP) solution that can be used to correct the position of any auto grade GNSS chipset. RTX Auto works in parallel with other on-vehicle sensors to deliver a positioning solution that satisfies ADAS and AD requirements.
Absolute position contributes to many features:
Lane centering. Systems designed to keep a car centered in a lane, relieving the driver of the task of steering, is often achieved with cameras and absolute position data. Absolute position can be used when lines disappear, or weather prevents them from being seen.
Map aiding. a combination of precise map and location data helps to navigate junctions, lane changes, roundabouts or intersections where lane information is essential to safe driving.
Prediction of future road structure. Both allow a vehicle to begin slowing in advance of a bend in the road and to avoid harsh braking that would happen if the system only relied on short range sensors.
Adhering to the speed limit. This helps drivers anticipate changes in speed limits when a downpour prevents cameras from seeing the speed limit signs or when they might be obscured by natural surroundings or another vehicle.
RTX Auto is both Automotive Safety Integrity Level (ASIL) and Automotive Software Process Improvement and Capability Determination (ASPICE) certified. These certifications validate that Trimble RTX Auto meets functional safety requirements for ADAS and autonomous applications in the auto industry.
Super Cruising. Trimble is on the road today providing RTX-based absolute positioning within General Motors’ Super Cruise driver assistance feature, a hands-free driving system for the freeway. For more information on Super Cruise, visit www.cadillac.com/world-of-cadillac/innovation/super-cruise.
A German research team successfully demonstrated a completely autonomous airplane landing in May, without assistance from any ground-based systems, fulfilling a key step towards autonomous air traffic and the much-bruited Urban Air Mobility (UAM).
An optical reference system, encompassing a camera in the normal visible range and an infrared camera for conditions with poor visibility, combined with GPS to bring the modified Diamond DA42 in for a safe, unpiloted landing at the Diamond Aircraft airfield in Wiener-Neustadt, Austria.
The team, from the Technical University of Munich (TUM) and the Technische Universität Braunschweig, formed the project they call C2Land with funding from the German federal government. Two 2019 conference papers by the researchers, cited at the end of this article, give the technical underpinnings of the C2Land system.
What’s New
Automatic landings by both commercial aircraft and small planes can and do take place at major airports with the Instrument Landing System (ILS) infrastructure to guide aircraft in with sufficient precision. Ground antennas send radio signals to the autopilot to make sure it navigates to the runway safely. Procedures in development to use GNSS alone to make autonomous landings also require a ground-based augmentation system.
But systems such as these are too expensive for small airports that will conceivably carry the major share of UAM: automated air freight transport and autonomous flying taxis.
What needs to happen before George Jetson air taxis become a reality? UAM will take place in the zone 500 to 5,000 feet above ground, transporting one to five passengers or cargo over distances of five to 50 miles. The vision shared by most UAM stakeholders, a group that includes NASA and the FAA, involves vertical take-off and landing rather than conventional “glide” takeoff and landing, but precise navigation to the landing spot is critical in both cases.
“Automatic landing is essential, especially in the context of the future role of aviation,” said Martin Kügler, research associate at the TUM Chair of Flight System Dynamics.
Fly-by-wire systems, semiautomatic and typically computer-regulated systems for aircraft navigation, use GPS signals for positioning. But since GPS is susceptible to errors, interference, and obstruction, it is not solely sufficient for landing procedures. Current GPS approach procedures require that human pilots resume control over the aircraft at 60 meters altitude, and land the aircraft manually.
To enable completely automated landings , the TU Braunschweig team designed an optical reference system: two cameras, one in normal visible range and one infrared camera for poor visibility conditions. Custom image processing software lets the system determine where the aircraft is relative to the runway based on the camera data it receives. Additional functions were integrated in the software, such as comparison of data from the cameras with GPS signals, calculation of a virtual glide path for the landing approach and flight control for various phases of the approach.
Visual Recognition
Test pilot Thomas Wimmer, who sat through the procedure with his hands folded, said “The cameras already recognize the runway at a great distance from the airport. The system then guides the aircraft through the landing approach on a completely automatic basis and lands it precisely on the runway’s centerline.”
The researchers presented their system in two papers at the Institute of Navigation’s 2019 Pacific PNT Meeting in April:
“Model-based Threshold and Centerline Detection for Aircraft Positioning during Landing Approach,” by S. Wolkow, M. Angermann, A. Dekiert, and Ulf Bestmann; and
“Linear Blend: Data Fusion in the Image Domain for Image-based Aircraft Positioning during Landing Approach,” by M. Angermann, S. Wolkow, A. Dekiert, U. Bestmann, and P. Hecker.
A report filed with NASA’s Aviation Safety Reporting System and published in June outlines how a passenger aircraft flew off course during a period of GPS jamming and nearly crashed into a mountain. Fortunately, an alert radar controller intervened, and the accident was averted.
Friedman Memorial Airport serves the ski resort town of Sun Valley, Idaho. Mountain peaks in the area are in excess of 12,000 feet. Airport arrival and departure procedures are carefully structured to ensure aircraft maintain safe distances from terrain.
According to the report, when “Aircraft X” arrived there was “…an abundance of smoke in the area” of the safe arrival route. Also “During this time there was widespread GPS jamming… Almost every aircraft was reporting…GPS outages.” Two previous flights had advised that their GPS signals were interrupted, but came back on line in time to make a safe approach to landing.
Aircraft X also reported problems with GPS, and then advised air traffic control that GPS had come back on line and was working well. The controller then cleared the aircraft for a GPS-based approach, including descending to 9,000 feet. Communications with and control of the aircraft was switched from Salt Lake Center (250+ miles away) to the tower at the local airport.
Shortly thereafter, the controller in Salt Lake City noticed Aircraft X straying off course. Also, it was at 10,700 feet altitude and nearing a 10,900 feet mountain. He quickly contacted the local control tower and the aircraft was directed back onto a safe flight path.
The report concludes that “Had [the Radar Controller] not noticed, that flight crew and the passengers would be dead, I have no doubt.”
Dana A. Goward is president of the Resilient Navigation and Timing Foundation.
GNSS chip manufacturers and positioning systems developers are working on bespoke devices for autonomous driving. This month, we look at a development with embedded functional safety.
By Fabio Pisoni, Domenico Di Grazia, Giuseppe Avellone, Luis Serrano, Brett Kruger, Laura Norman and Natasha Wong Ken
INNOVATION INSIGHTS by Richard Langley
I DRIVE A 10-YEAR OLD KIA SPORTAGE. It is still quite roadworthy despite having to contend with New Brunswick winters. However, it lacks some of the safety features that are present in newer cars. There is no back-up camera, no forward-collision warning, no automatic emergency braking, and no blind-spot warning, for example. These are just some of the safety systems that come as standard or optional on most new cars these days. Still, the driver is responsible for the safety and operation of the car at all times. True, help might be provided for parallel parking and cruise control, but that’s about it for automated operation with most vehicles.
But things are changing and changing fast. Real automation is coming to automobiles. Already partial automation is available in some high-end vehicles that can take over steering, braking and acceleration in certain circumstances. The driver is still responsible for other aspects of the vehicle’s operation including paying attention to road conditions. Soon, we will have conditional automation where the car can drive itself but the driver must stay alert and be prepared to take over immediately at any time. Next will come high automation where a computer fully drives the car at certain times on certain routes such as a highway. Multiple systems, including back-up systems, will maintain a required safety level and the car will determine if it is safe to operate autonomously. If not, it could pull over to the side of the road and shut down. And finally, we may have full automation of cars. They will be able to drive on any road under virtually any conditions and won’t need any controls such as steering wheels or accelerator or brake pedals.
Augmented GNSS guidance will play a major role in the automation of vehicles. As with any navigation or guidance system, there are four important requirements: accuracy, availability, continuity and integrity. Perhaps the most obvious requirement, accuracy describes how well a measured value agrees with a reference value, typically the true value. How well a system accounts for various errors or biases determines the accuracy of corrected measurements and, ultimately, the accuracy of a derived position. A navigation system’s availability refers to its ability to provide the required function and performance within the specified coverage area at the start of an intended operation. In many cases, system availability implies signal availability. Environmental factors such as signal attenuation or blockage or the presence of interfering signals might affect availability. Ideally, any navigation system should be continuously available to users. But, because of scheduled maintenance or unpredictable outages, a particular system may be unavailable at a certain time. Continuity, accordingly, is the ability of a navigation system to function without interruption during an intended period of operation.
While accuracy, availability and continuity of a guidance system are all important, it is the integrity or trustworthiness of the system that is paramount. It is why the automotive industry has already developed integrity standards for the automation of vehicles. And it is why GNSS chip manufacturers and positioning systems developers are working on bespoke devices for autonomous driving, whatever the level of automation. In the Innovation column this time around, we’ll learn about one such development — one with embedded functional safety.
Autonomous driving applications are raising the requirements for onboard GNSS receivers to new highs. Position accuracy, protection levels, high availability, robustness of operation and integrity are the priorities shaping a new class of automotive components and architectures. Autonomous driving deals with life-critical issues: the expectation of reliability and safety for this new generation of receivers, as well as for other sensors and systems, is very high.
The International Organization for Standardization (known by the language-independent short form ISO) has issued documents codifying functional safety (FuSa) for automotive applications: ISO 26262: part 1 to part 11. ISO 26262 complements the well-known automotive reliability standard published by the Automotive Electronics Council, AEC-Q100. With respect to FuSa, a system can be defined as functionally safe if it always operates correctly and predictably. More importantly, in the event of failures, the system must remain safe for people. Lastly, as security is becoming paramount, a new standard for cybersecurity in automotive applications — ISO/SAE 21434 — is in development by ISO and SAE International (initially called the Society of Automotive Engineers) that will require a GNSS receiver to be robust against jamming, spoofing and meaconing attacks.
The Automotive Safety Integrity Level (ASIL) is a key part of ISO 26262 compliance, and the standard specifically identifies the minimum testing requirements depending on the ASIL of the component. The ASIL of a component or system depends on the ASIL of the target application. The ASIL is determined at the beginning of a development process. It varies from ASIL-A to ASIL-D, where A is for less critical applications and D for the most critical ones such as steering and breaking systems. ASIL-rated lane-level positioning performance can be demonstrated today by combining an ASIL-B software positioning engine and TerraStar-X correction technology from Hexagon Positioning Intelligence with GNSS measurements from an ASIL-B-rated GNSS chipset.
To conjugate performance requirements with the demand of embedded functional safety, STMicroelectronics has developed TeseoAPP (STA9100), a next-generation GNSS component, designed to meet an ASIL-B level of safety. TeseoAPP is a multi-band GNSS measurement engine. It outputs all the observables, navigation and integrity data required by a safety-critical precise positioning algorithm, located on a host processor. TeseoAPP also computes a local L1 code-based standard position, velocity and time (PVT) solution (SPS) for monitoring and integrity purposes. Also part of the baseline features are autonomous satellite acquisition (cold start condition), real-time assistance, data decoding and storage on external non-volatile memory (NVM), accurate timing and pulse-per-second generation under vehicle dynamics.
RECEIVER ARCHITECTURE
The target architecture for a safety-critical platform is sketched in FIGURE 1, where a host microprocessor is in charge of collecting GNSS observables and sensor data from the TeseoAPP. The latter includes on the same chip die a first configurable RF chain for the L1 signal ensemble and the baseband part for processing all the signals in the served bands, while the second chip is an RF front end (code-named STA5635), configurable for receiving the other served bands (such as GPS L2 or L5, Galileo E5a or E5b or E6, and so forth). The two chips are clearly visible in the photograph of a TeseoAPP evaluation module of FIGURE 2.
FIGURE 1. Block diagram of the TeseoAPP platform for safety-critical applications, featuring surface-acoustic-wave (SAW) filters, a temperature-compensated crystal oscillator (TXCO), non-volatile memory (NVM) and both internal and external STA5635 tuners. (See text for other initialisms used.) Diagram: Authors)FIGURE 2 The TeseoAPP Evaluation Module, including the STA9100 (TeseoAPP) and STA5635 (external tuner). Photo: Authors
The selected frequency plan and constellation configuration depend on the specific autonomous driving scenario and the target geographic area. The TeseoAPP supports a mix of frequencies and signals as shown in TABLE 1. The chipset baseband unit can track up to 80 channels. A tracking snapshot from a rooftop antenna (located at the ST office in Naples, Italy) is illustrated in FIGURE 3.
Both the TeseoAPP and the STA5635 have been designed for ASIL-B following the concept of “safety element out of context” (SEooC) described in ISO standard ISO 26262:2012. In this context, assumptions have been made for the application (such as on the mission profile), identifying the related safety goals from which functional and technical safety requirements have been derived.
TABLE 1. The TeseoAPP (STA5635) supported frequency plans and scenarios.FIGURE 3. Screenshot of the L1-L5 TeseoAPP configuration, from the ST Teseo-Suite tool (using the Naples rooftop antenna). Image: Authors
Following the guidelines identified in the ISO 26262 flow for safety-relevant product development, several safety mechanisms have been identified at the hardware, firmware and system/boot level. The microcontroller unit (MCU) supports dual-core operation in a lock-step configuration to verify processor output errors together with a memory built-in self-test (executed at startup) and error correction code on a safety-related embedded random access memory. Other hardware redundancies have been introduced in safety relevant parts such as triple-voted registers for critical configuration parameters. For the real-time operating system (RTOS), an ASIL-D-level product — the highest level — was selected.Functional safety analysis of the GNSS sub-system has produced a dedicated technical safety concept, including aspects such as tuner operation, interference and jamming mitigation, signals and observables quality management (QM), reliable host communication (using generic end-to-end or E2E protocols for data integrity and resilient flow control), and reliable system software. A simplified overview of all these safety mechanisms is outlined in FIGURE 4, where the orange-colored blocks are specific for the GNSS sub-system.
FIGURE 4. Overview of the TeseoAPP safety mechanisms. (See text for acronyms and initialisms used.) Diagram: Authors
Safety Mechanisms. The technical safety concept of the GNSS sub-system is implemented by a security, integrity and safety (SIS) monitoring layer (see FIGURE 5). The SIS collects information and metrics from other receiver blocks embedded in the RF/baseband hardware and from different components of the GNSS firmware stack. The SIS internally computes integrity risk estimates, which are delivered to a central intelligence monitor (CIM) capable of switching the receiver into a safe state, within a fault-tolerant time interval, when the overall receiver integrity appears compromised. In its simplest form, the CIM can be represented by a weighted sum of integrity risk inputs, followed by some activation function. During this process, a first layer of logic (CIM-L1) combines a subset of signal quality metrics to decide a priori which observables shall be passed to the host or discarded (not delivered).
FIGURE 5 Safety information flow through the TeseoAPP security, integrity and safety layer. (IP = intellectual property; other short forms in text.) Diagram: Authors
The collected signal metrics include quality estimators (based on multi-correlation techniques for example) or classic linear combinations of observables (such as dual-frequency carrier-phase differences or code-minus-carrier). Receiver metrics, on the other hand, have a more global scope and include estimators for inter-frequency biases, system-time cross-checks among constellations, and so on. The fault collection and control unit (FCCU) conveys hardware status flags to the SIS. Typically, an FCCU exception indicates some critical hardware failure and takes a priority path when switching the safe state. For example, a fault in the MCU lock-step monitor will trigger an immediate firmware action, mediated by the FCCU.
POSITIONING PERFORMANCE
To demonstrate the performance that can be achieved using the ST TeseoAPP chipset, Hexagon Positioning Intelligence (PI) has combined measurements from the TeseoAPP with an automotive-grade antenna and Terrastar-X correction technology, and processed the data using Hexagon PI’s software positioning engine. Even with a modern receiver supporting dual-frequency, multi-constellation measurements, such as the TeseoAPP, corrections are necessary to deliver decimeter-level performance and safety information required by an autonomous vehicle.
In clear-sky environments, lane-level positioning accuracy is achieved, enabling GNSS as a key input to autonomous systems. FIGURE 6 shows the horizontal error performance of the combined ST+PI solution in the form of an error time series and an error cumulative distribution function (CDF). The error performance expected from today’s single frequency automotive-grade GNSS without corrections and processing is also shown for comparison.
FIGURE 6. Horizontal error time series and cumulative distribution function (CDF) of the TeseoAPP alone and of the TeseoAPP with Hexagon PI software positioning engine (SWPE) in an open-sky environment. (Image: Authors)
For guidance systems in autonomous applications, the GNSS position must be accompanied by safety information and integrity guarantees. The concept of protection levels (PLs) has been introduced to provide this. A horizontal protection level defines a circle or ellipse around the reported GNSS position, which will have some error, within which the actual position is guaranteed to fall. The Hexagon PI software positioning engine is ASIL-B rated, so its position and PL outputs are available for use in safety-related autonomous applications. The autonomous system using the GNSS position is assured that its actual position is within the protection level ellipse. To output ASIL-B-rated positions accompanied by PLs, ASIL-rated GNSS measurement inputs are required.
Using the inputs and techniques described above, the Hexagon PI software positioning engine calculates PLs for every GNSS position output. The Hexagon PI data from Figure 6 is shown again in FIGURE 7 with accompanying PL information. In this case, a PL with integrity risk of 10-7 is shown, meaning that the actual position error is expected to exceed the reported PL at a rate less than 10-7 per hour.
FIGURE 7. Horizontal error and protection level (PL) including cumulative distribution functions (CDFs) of the Hexagon PI software positioning engine (SWPE) in an open-sky environment. (Image: Authors)
The PLs shown in Figure 7 are typically much greater than the position error. This is because the protection level calculation must account for a large number of potential faults that are not generally present. For instance, undetectable GNSS satellite faults can occur at rates greater than 10-7 per hour, and so must be accounted for in the PL.
In non-clear-sky environments, the GNSS position calculation is complicated by frequent loss of “sight” of the GNSS satellites. This is mitigated by having additional constellations and frequencies. However, for added availability of a precise position in challenging environments, it is necessary to incorporate sensor fusion into the position calculation, typically by using a six degree-of-freedom inertial measurement unit (IMU) as input, which includes three accelerometers and three gyroscopes to measure 3D translational and rotational motion. The IMU can maintain position accuracy for short periods when GNSS is unavailable, such as when driving under an overpass on a highway. The IMU provides a relative positioning output, so the absolute error growth is unconstrained in the absence of GNSS inputs. Therefore, it is important to have the GNSS receiver as the primary sensor in the positioning solution to constrain IMU drift and to reacquire GNSS signals rapidly after emerging from a GNSS outage.
Position error results for a typical highway environment are shown in FIGURE 8 after adding input from an automotive-quality IMU to the Hexagon PI software positioning engine. Small spikes in position error are due to short GNSS outages along the test route. However, the error growth due to loss of GNSS is minimal due to the coupling of the IMU data with the GNSS measurements.
FIGURE 8. Horizontal error time series and cumulative distribution function (CDF) of the TeseoAPP alone, and of the TeseoAPP with Hexagon PI software positioning engine (SWPE) in a highway environment. (Image: Authors)
FIGURE 9 shows the Hexagon PI highway data with accompanying PLs. Though the errors are well-constrained through GNSS outages, the PLs typically increase significantly. This is due to the higher noise of low-cost IMUs, and the uncertainty associated with reacquiring GNSS signals. PLs must account for worst-case IMU performance, which can have errors orders of magnitude greater than the nominal performance. During GNSS signal reacquisition, minimizing receiver noise is critical for fast position reconvergence, reinforcing the need for high-quality GNSS in autonomous applications.
FIGURE 9. Horizontal error and protection level (PL) including cumulative distribution functions (CDFs) of the Hexagon PI software positioning engine (SWPE) in a highway environment. (Image: Authors)
CONCLUSION
The TeseoAPP is the first generation of multi-band GNSS chipsets designed by STMicroelectronics to meet the two main requirements of autonomous driving: accuracy and safety-critical operation. The execution of the ISO 26262 standard for TeseoAPP is still a work in progress and encompasses two main aspects: 1) a safety plan implementation, code quality metrics and processes management and 2) the technical safety concept. Both of these aspects presented specific challenges, mainly due to the inherent complexity of the product and the large amount of firmware involved.
To exploit the maximum benefit of the TeseoAPP in safety-critical automotive applications, a high-accuracy ASIL-B-rated position engine is required. Hexagon PI’s software positioning engine is designed to use measurements from an ASIL-rated GNSS receiver, along with GNSS corrections and IMU data, to generate ASIL-rated position outputs, with accompanying integrity guarantees. The Hexagon PI software positioning engine computes protection levels. The calculation and determination of PLs is required to meet the safety and integrity guarantees necessary in autonomous driving for functionally safe operation.The software positioning engine also outputs ASIL-rated velocity, attitude and absolute time data, although we have not discussed these in this article.
The required high performance and safety expectations suggested, since the early stages of the project, a system composition in which the TeseoAPP was configured as an ASIL-B measurement-engine whereas the ASIL-B software positioning engine algorithms (by Hexagon PI) run on a separate ASIL host processor. We believe this synergy of competencies will represent the key for a successful solution to enable safe and reliable positioning in autonomous driving applications.
ACKNOWLEDGMENTS
The TeseoAPP chipset has been developed with the support and in the framework of the European Safety Critical Applications Positioning Engine project, which is funded by the European GNSS Agency under the European Union’s Fundamental Elements research and development program.
FABIO PISONI leads the GNSS System Architecture and Software Team (Automotive and Discrete Group) at STMicroelectonics Italy in Milan, where he has worked since 2009. He has a degree in electronics from Politecnico di Milano and has previous experience as a GNSS and digital signal processing (DSP) engineer.
DOMENICO DI GRAZIA is a GNSS signal senior staff engineer at STMicroelectronics Italy in Naples, where he has worked since 2003. He has a degree in telecommunication engineering from the University of Naples Federico II, holds patents in the GNSS area, and has previous experience in digital communications.
GIUSEPPE AVELLONE is in the GNSS System Architecture and Software Team (Automotive and Discrete Group) at STMicroelectonics Italy in Catania, where he has worked since 1998. He has a degree in electronics from Università di Palermo and previous experience as a GNSS and DSP engineer.
LUIS SERRANO is a GNSS technical marketing manager with STMicroelectronics based in Munich. He holds a Ph.D. in GNSS from the Department of Geodesy and Geomatics Engineering, University of New Brunswick, Canada. He has been active in the GNSS precise positioning field since 2007, and holds a patent on GNSS.
BRETT KRUGER is a software engineer specializing in GNSS/INS integration in the Safety Critical Systems Group at the Hexagon Positioning Intelligence (PI) NovAtel brandin Calgary, Canada. He holds an M.A.Sc. in electrical engineering from the University of Toronto, Canada.
LAURA NORMAN is a geomatics engineer specializing in GNSS integrity and protection levels in Hexagon PI’s Safety Critical Systems Group. She obtained her M.Sc. from the Department of Geomatics Engineering at the University of Calgary, Canada.
NATASHA WONG KEN is the Safety Critical Systems product manager at Hexagon PI. She has worked at Hexagon PI since 2012 after obtaining a B.Sc. in geomatics engineering from the University of Calgary.
