Today, many field operations — sowing, tilling, planting, cultivating, weeding and harvesting — rely on satellite-based autonomous guidance technology for agricultural machines. Yet farmers are still challenged by poor signal tracking, signal interference, communication instability and heading inaccuracy in tough environments, such as on uneven ground or slopes or under dense tree canopy. Because of insufficiently advanced navigation technology, ordinary machines fail to achieve the high efficiency expected and might even cause safety hazards. Therefore, the market has been awaiting a high-performance smart antenna with centimeter-level accuracy.
Harxon’s Smart Antenna TS112 PRO provides scalable and reliable positioning solutions for tough agricultural environments, such as uneven ground or fields with underground cables, as well as complicated weather conditions, including rain, fog and dust clouds.
The TS112 PRO integrates in one compact enclosure Harxon’s four-in-one GNSS/4G/Bluetooth/Wi-Fi antenna and a Hexagon | NovAtel OEM GNSS module. The multi-constellation GNSS antenna is designed with Harxon X-Survey technology and features multi-point feeding with high gain and wide beam width, which ensures high phase-center stability for ultimate RTK centimeter-level positioning accuracy. This is realized by subscribing to the Ntrip service via the LTE network to receive corrections or by setting up a local base station to broadcast corrections by radio.
The Hexagon | NovAtel OEM GNSS module is default-enabled for RTK, offering precise positioning and advanced interference mitigation for space-constrained applications and challenging environments. Additionally, users can achieve globally available centimeter-level positioning accuracy by using TerraStar satellite-delivered L-band correction services, with no need to set up an expensive network infrastructure.
TS112 PRO guarantees pass-to-pass accuracy down to 20 centimeters, where relative positioning is critical. It can also provide smoother steering and straighter rows by reducing positioning jumps that might occur during RTK signal outages or when a smart antenna changes positioning modes. Its terrain compensation algorithm is capable of correcting deviations caused by a vehicle’s roll and pitch while working on uneven ground or slopes.
Automated steering systems have been widely deployed in advanced industrial countries and on large farms to improve agricultural productivity. However, technological and price barriers have constrained their wider adoption. Reliable RTK positioning and the expected accuracy of automated steering systems enable farmers to optimize their work efforts while reducing input costs and fuel consumption.
CHCNAV customer Niva LLC in Voronezh, Russia, was particularly interested in acquiring an automated steering system able to provide consistent high accuracy, even in scattered fields over long distances and with unstable coverage for GSM (the Global System for Mobile communication, a cell phone standard used in most of the world). Some systems Niva tested would lose GNSS RTK network correction signal reception while working in difficult terrain with gullies. A dual GNSS RTK correction source was therefore a key technical feature to ensure uninterrupted auto-steering operation in all terrain configurations.
The CHCNAV NX510 SE’s built-in connectivity modules include a 4G modem and an additional UHF radio module to allow farmers to work with RTK correction sources from local RTK networks or GNSS RTK base stations for no additional cost. As a result, the NX510 SE can receive GNSS RTK corrections from various GNSS network operators as well as from a local radio modem input to compensate for possible poor GSM coverage. The system’s combined GNSS+INS terrain-compensation technology ensures automated steering accuracy of 2.5 centimeters and offers excellent performance in ditching, seeding and harvesting applications.
Niva also wanted an auto-steering system that could be quickly and easily mounted on a variety of tractors and other farm vehicles at a price that would allow for rapid return on investment. The NX510 SE can be moved from one tractor to another in less than 40 minutes, as farming operations change. The software’s user interface for controlling field operations is designed for both experienced and casual users to allow even greater flexibility.
Intelligent navigation-based automation is redefining the farmer’s humble tractor to robotic status. This results in significantly faster field preparation and cropping and dramatically reduced labor costs.
Any autonomous vehicle requires the highest levels of navigational accuracy, control and safety. For farming applications, this typically means maintaining exact heading at very low speeds, often over bumpy terrain. These requirements make using the right navigational equipment critical to success. The key challenge is maintaining precise placement and movement of the tractor relative to crop rows and field boundaries. Failure to maintain precision can cause rows to be damaged or planted seedlings to be uprooted. The typical accuracy required for precision farming is position to within a decimeter (10 cm) — well beyond basic GNSS. This requires real-time kinematic (RTK) positioning and advanced signal processing.
Sabanto, a U.S.-based farming as a service (FaaS) start-up, was facing this exact challenge. The company needed a precise and reliable navigation solution for its fleet of driverless tractors deployed in a growing number of U.S. states, including Illinois, Iowa, Nebraska and Minnesota.
“The reliability of Advanced Navigation’s GNSS Compass gave us the peace of mind required to operate fully autonomously from Spring to Fall of 2020,” explained Craig Rupp, CEO of Sabanto.
Thanks to its dual-antenna GNSS and RTK corrections, the GNSS Compass can offer high-accuracy heading. Accurate position is maintained using real-time correction data, delivered from nearby ground base stations, resulting in near-centimeter accuracy under the most demanding conditions.
Furthermore, the GNSS Compass includes an integrated inertial navigation system (INS) to ensure consistent position accuracy of the tractor in the event of degraded or lost signals from GNSS satellites from heavy canopy or steep terrain. Roll, pitch and heading data also improve the stability of the autonomous platform over difficult terrain.
Sabanto engineers can now deploy and remotely monitor their fleet of autonomous tractors 24/7. Operators can simply pre-program the itinerary and field boundaries, as well as when to lift and lower tillers, resulting in the tractors planting up to two hectares (five acres) per hour.
A PNT expert suggested that my piece titled “Opposite and Complementary: eLoran is part of the solution to GNSS vulnerability” in our November 2021 issue could be augmented with information not currently available on the proposed eLoran capability. This expert also questioned my statement that eLoran “does not have any common failure modes with GNSS” and pointed to potential common threats such as from cyberattacks, physical attacks, and space weather.
Matteo Luccio
I welcome such feedback on the contents of these pages — and agree that in this case some hard questions are warranted. So, in the interest of further exploring the use of eLoran, I pose some questions, hoping that its advocates will provide answers. I know that at least some of them will not shy away from this challenge.
Please note that I wish to keep the discussion on positioning, not the easier question of timing, because that was the primary focus of my article. I also wish to address long-term outages (weeks or months), which would have a greater impact on the United States.
Some of these questions have been addressed, at least in part, in various studies and proposals, most of them now more than a decade old. So, it would be helpful to update those answers and consolidate them in the pages of this magazine.
1. Accuracy specifics. While my November article stated that eLoran would have a two-dimensional accuracy of “better than 20 meters, and in many cases, better than 10 meters,” is that RMS, 95%, or some other statistic?
2. Performance standard. GPS provides a commitment to users in a published performance standard. What specific measures of positioning accuracy, integrity and continuity would you recommend the proposed eLoran system be committed to provide (using the architecture described in the answer to Question 6)?
3. Coverage. Would you recommend this eLoran positioning performance hold for the entire United States (including Alaska, Hawaii, Puerto Rico and other territories), only for the “lower 48” states, or only parts of these 48 states?
4. Current users. By number of users, the predominant common current civil uses of GNSS for positioning are consumer devices (mostly cellphones). By contribution to the U.S. economy, the predominant uses are high-precision applications. For what fraction of these uses would eLoran positioning be adequate? Could an eLoran receiver and antenna fit in today’s consumer devices?
5. Future uses. Emerging civil uses of GPS for positioning include autonomous ground and air vehicles, navigation to space and in space, and lane-accurate car navigation. Which of these could be served by eLoran?
6. Architecture. To maintain accuracy during a prolonged GPS outage, eLoran would require reference stations to calibrate time-varying propagation errors, as well as a certain number of transmitters for good nationwide geometry and for redundancy, ensuring service even if a transmitter is attacked or is taken off-line for maintenance. What architecture would you recommend to achieve this?
7. Infrastructure cost. What would be the cost of installing the required transmitters, power supplies, reference stations, communication links and control system for the architecture described in the answer to Question 6? Can you reference a recent and independent estimate? To a ballpark figure, what cost fixed-price contract would you accept to implement it? Similarly, what would be the annual costs for operating and maintaining this infrastructure?
8. Impact. eLoran transmitters are large and high-power. Providing positioning across the United States could require building some of them from scratch or significantly reconstructing old Loran sites. What issues — such as environmental, aviation safety and security — would this raise, and how would you recommend they be addressed?
9. Receivers. Assuming all the above were achieved, it would accomplish nothing unless eLoran receivers were widely purchased, installed and used. How much would that cost? Who would pay? Should we assume that “if we build it, they will come”?
10. Alternatives. Given the widespread development of other positioning technologies over the past decade, much has changed since the earlier recommendations for eLoran. How do we know that eLoran is the right investment — or even a needed part of the solution or needed system in a system of systems — for the future of U.S. PNT?
Common threats to GNSS and eLoran could include the following:
1. Cyber attacks. Given that GPS’s OCX is said to be the most cybersecure system built by the U.S. Department of Defense, how would eLoran’s control system be even more cybersecure than OCX, to avoid a common cyber-vulnerability?
2. Physical attacks. Given concerns about possible physical attacks on GPS satellites, which move at multiple km/sec 20,000 km from Earth, would it not be easier to physically attack eLoran transmitters, which are stationary, terrestrial, in remote locations, and hundreds of feet tall and require massive power sources?
3. Space weather. GPS is potentially vulnerable to severe space weather that could damage satellites or temporarily hinder signal propagation from space to Earth. However, severe space weather could also damage the power grid upon which megawatt eLoran transmitters rely. How would eLoran service be protected from the effects of severe space weather, such as a Carrington Event?
Send me your thoughts at the e-mail address below, with “eLoran” in the subject line.
From its very first issues, 31 years ago, this magazine has covered the role of GPS, now GNSS, in guiding ships, trains and automobiles. What were then some of the most aspirational visions of future applications are now routine. For all forms of transportation, navigation is a safety-critical issue. This is particularly true in the case of cars on public roads, which is also where the technical challenges are the greatest. Ships mostly travel in deep waters, far away from other traffic and fixed obstructions, and nearly always enjoy an unobstructed line-of-sight to GNSS satellites. So do trains, which have the additional advantages of being kept, literally, on track and of operating in controlled environments, with hardly any concerns for unexpected intrusions on their path. Cars, trucks, and busses, on the other hand, must contend with many other vehicles, including those with distracted, drowsy, drunk, or drugged drivers, as well as cyclists, pedestrians, accidents, construction and a bedeviling myriad of sudden and often unpredictable circumstances. Additionally, their view of the sky is often limited by overpasses, tunnels and tall buildings, which challenge GNSS-based navigation with signal occultation and multipath, and their view of their surroundings is often blurred by weather conditions.
Currently, prototype autonomous vehicles carry cameras, lidar scanners, radars and ultrasonic sensors to provide positioning relative to mapped features, as well as for collision avoidance. However, some use cases require absolute positioning sensors, consisting of GNSS receivers coupled with inertial sensors. For example, autonomy levels 3 and 4 require dynamic error bounds of no more than a few meters most of the time under challenging highway conditions and levels 4 and 5 will require this level of accuracy even in deep urban canyons.
This month’s cover story highlights progress in several transportation-related GNSS/PNT applications
Autonomous vehicles require lane-level accuracy at all times and in all conditions. However, under many conditions, such as in urban canyons and tunnels, they may lose line-of-sight to enough GNSS satellites to achieve accurate and robust positioning or may have no signal at all. In these situations, they need data from other sensors, including an odometer and an inertial measurement unit (IMU). Creating reliable and safe autonomous navigation requires fusing GNSS and inertial technology in a multi-layered system.
SBG Systems and its partners LeoDrive.ai and Intempora, have been doing this to develop solutions for autonomous vehicles. SBG’s technology enables multi-sensor integration while addressing such autonomous navigation challenges as time synchronization, integrity, precise positioning and high-definition mapping.
“To ensure performance and build trust, we assemble our own IMUs from carefully selected industrial-grade parts, then we calibrate all our products individually,” said Laurent Le Thuant, business manager for SBG, in a recent webinar.
For safe operation, Le Thuant explained, the vehicle’s true positional error (PE) must be smaller than its protection level (PL), which in turn must be smaller than its alert limit (AL): PE < PL < AL. Otherwise, the solution is declared unavailable or reports misleading information.
In automotive tests conducted in a business district near Paris, an SBG vehicle was equipped with both a GNSS-only, automotive-grade multiband RTK receiver equipped with a PL determination algorithm and an RTK GNSS receiver tightly-coupled with an IMU and an odometry input. A comparison showed that the former was not suited for self-driving, while the latter significantly improved the solution availability, accuracy and protection levels.
For self-driving in the most severe conditions, even this solution requires integration of supplementary sensors, such as cameras, lidars and radars for precise localization.
SkyTraq Technology, a fabless semiconductor company, develops GPS/GNSS chipsets and modules for meter-level accuracy vehicle navigation and tracking applications and for centimeter-level accuracy real-time kinematic (RTK) surveying and precision guidance applications.
Photo: SkyTraq
The company’s chipset design is driven by market trends, said Oliver Huang, the company’s general manager. He explained the company has moved from single-frequency to dual-frequency devices.
SkyTraq’s chipset is designed to be common hardware for different target applications enabled by customized software. Traditionally, in the automotive market, vehicle navigation systems have relied on fusing GNSS receivers with dead-reckoning technology that uses micro-electromechanical (MEMS) inertial measurement units (IMUs) and wheel-tick data.
“We are now seeing more aftermarket vehicle tracking applications that take advantage of superior GNSS/DR performance using untethered dead-reckoning technology that uses sensor fusion of GNSS receiver and MEMS IMUs without the need for wheel-tick data,” Huang said. “GNSS receivers with decimeter or better accuracy, combined with dead-reckoning that uses low drift IMUs, will be important in emerging autonomous vehicle applications.”
SkyTraq’s PX100 chipset for L1 meter-level accuracy applications and centimeter-level accuracy RTK applications uses L1 and L1/L2 signals from all four major GNSS constellations (GPS, GLONASS, Galileo and BeiDou).
Because of the trend toward high-precision, which requires good carrier-phase raw measurement data, the biggest challenge in receiver design is with the antenna, Huang explained. “Using an advanced semiconductor process, one can have low power, small size chipsets taking advantage of all the available GNSS signals, yet there is no small antenna capable of producing high-quality carrier phase data for high-precision GNSS applications. So far, we have only seen bulky RTK antennas capable of generating high-precision results.”
Swift Navigation designs, manufactures and integrates GNSS receivers, as well as providing the Skylark wide-area GNSS corrections service. Its markets are automotive, transportation (last mile delivery, commercial trucking, rail), robotics/machine control (construction, mining, precision agriculture, landscaping), UAVs, micromobility and mobile devices and applications.
The company’s technology is compatible and interoperable with most major GNSS receivers for multiple markets. Its Starling positioning engine and Skylark corrections “are scalable to bring precision to legacy low-cost single-frequency receivers, all the way to the most sophisticated state-of-the-art triple-frequency multi-constellation systems,” said Joel Gibson, Swift’s executive vice president of Automotive. “By working with a multitude of receiver vendors for different applications, Swift leverages all constellations and all signals and maximizes the performance required for the application.”
The most accurate and reliable navigation system for every application would take advantage of all available GNSS signals, as well as all available corrections, dead reckoning and fused data from other sensors, such as cameras, lidar and radar. However, of course, that is not possible due to cost, size, weight and power considerations. Swift’s approach to the trade-offs required depends on each use case.
Micromobility
In the area of micromobility (such as scooters), the main constraints for implementing a positioning solution are cost and power, coupled with the challenge of satellite signal outages and multipath in dense urban environments where these vehicles primarily operate, Gibson explained. “Cost-effective dual-frequency GNSS receivers are now showing up in micromobility architectures. Pairing them with our Starling positioning engine, which integrates inertial sensor data and wheel ticks, and augmenting them with Skylark corrections data, makes it possible to meet such compliance requirements as geofencing and limiting sidewalk use.”
Additionally, by achieving decimeter-level positioning, Swift’s micromobility solution makes it easier for both users and service staff to find scooters, which increases the scooter companies’ revenues.
Photo: Swift Navigation
Automotive
In the automotive industry, inertial sensors and wheel odometry are ubiquitous and pair naturally with GNSS to mitigate satellite signal outages, Gibson pointed out. Likewise, cameras and radar — cornerstones of ADAS — are very complementary to GNSS for safety applications, and lidar further complements GNSS in feature-rich environments such as dense urban areas.
Rail
Rail applications, such as Positive Train Control, have traditionally needed an accuracy of one or two meters, coupled with ruggedized hardware. “Swift’s precise positioning solution is deployed across continental rail systems today, and we are now engaging rail OEM and operator programs requiring sub-meter accuracy to ensure track-to-track accuracy and safety requirements in support of the transition to more autonomous rail operations,” said Gibson. “Leading rail companies are also looking for operational efficiencies by transitioning away from the high operational costs of maintaining reference base stations along track routes, instead moving to the more cost effective, reliable and seamless Skylark corrections coverage.”
Flipping the traditional scenario, in which car racers risk their lives on a racetrack, the Indy Autonomous Challenge (IAC) aimed to help save lives by improving collision avoidance systems, train future automotive engineers, and make the public more comfortable with autonomous cars. Held Oct. 23 at the Indianapolis Motor Speedway and organized by Energy Systems Network, the race saw 21 universities from nine countries forming nine teams to compete for a $1 million grand prize. Following in the footsteps of the DARPA Grand Challenge, first held in 2004 and later renamed the DARPA Urban Challenge, the IAC was the world’s first high-speed autonomous race. The winning team was TUM Autonomous Motorsport from the Technical University of Munich, Germany.
All competing teams were given the same identical vehicle to work with, a Dallara AV-21, modified to carry no one in the cockpit and equipped with two Hexagon | NovAtel PwrPak7-Ds multi-frequency, multi-constellation GNSS receivers, six cameras (two of which faced backward), three lidar scanners and four radars. Each team had to develop its own autonomy-enabling software stack, including the algorithms and neural networks. All the components, except the computer, had to be commercial-off-the-shelf, available on the market. No sensors could be custom-made.
Since 2001, Dallara has been the sole supplier of the Indy Lights series, a championship to prepare drivers for the NTT IndyCar Series. The Dallara AV-21 is a collaboration between Dallara’s Italian headquarters in Varano Melegari (Parma) and Dallara IndyCar Factory in Speedway, Indiana. The new car offers a modern, stylish appearance and provides the proper training required for drivers as the final step on the ladder to the NTT IndyCar Series.
The process by which the automated vehicle sensors and computers were fused into a singular package and integrated into the AV-21 was led by Clemson University’s International Center for Automotive Research’s Deep Orange 12 (DO12) project. The Deep Orange process mirrors that of automotive original equipment manufacturers (OEMs), and the DO12 project scope allowed for engineering and innovation across multiple subsystems. Student groups within the DO12 team explored solutions within and across multiple subsystems, including:
vehicle-to-vehicle communications
perception systems
onboard computing
drive-by-wire chassis control systems
vehicle dynamics
vehicle-to-infrastructure communications
powertrain design and integration
vehicle demonstration based on high precision GPS.
Hexagon’s Autonomy & Positioning division provided GNSS receivers and subject-matter experts to the Deep Orange 12 team. The team architected the sensor kit for the Dallara reference vehicle, which AutonomousStuff then replicated 10 times. The team did not compete in the IAC to avoid a conflict of interest and allow students to work closely with competitor teams from universities around the world. The PwrPak7-E1 contains a MEMS IMU to deliver Hexagon | NovAtel’s SPAN technology, a deeply coupled GNSS + inertial engine in a single-box solution. Each GNSS receiver has two antennas to provide heading. The Deep Orange 12 team used HxGN SmartNet RTK corrections, which brought the accuracy down to a few centimeters.
Without developing a driverless decision-making algorithm, Clemson students tested the vehicle with the help of a high-precision positioning system. They developed a control algorithm that can track the optimal line around the Indianapolis Motor Speedway such that all vehicle systems could be validated in a simulated racing environment. Data from these tests were shared with the competition teams to aid in their development of driverless algorithms.
Energy Systems Network will host a head-to-head, high-speed autonomous racecar passing competition at the Las Vegas Motor Speedway on Jan. 7, 2022, during the Consumer Electronics Show. Several of the teams that competed in the IAC, including the winner and finalists, will participate. The primary goal is to advance technology to speed commercialization of fully autonomous vehicles and deployments of advanced driver-assistance systems.
Swiss company u-blox designs and manufactures GNSS receivers used in the automotive market, including driverless cars, and for micro-mobility devices, such as the Bird scooter.
In deep urban canyons, the biggest challenge for positioning cars is achieving sufficient accuracy despite multipath, said Aravinthan Athmanathan, product manager for the company’s Automotive GNSS line of receivers. “The challenge for autonomous driving is reliable lane-accurate positioning and integrity.”
The company develops its own dead-reckoning algorithms, which use data from an inertial measurement unit (IMU) and wheel speed sensors. “We also provide dual output, so the end customer can choose whether to use GNSS only or a sensor-fused solution,” said Athmanathan. This is especially challenging at the sub-meter accuracy level.
Different Uses, Different Sensors
Different automotive use cases require different GNSS receivers. To meet this challenge, u-blox offers the NEO-M9L for standard precision and the ZED-F9K for high precision, depending on the customer’s needs. Additionally, it is investing a lot “in functionally safe GNSS and in being the GNSS enabler for car manufacturers,” said Karin Steinhauser, the company’s senior marketing communications manager.
For navigation with meter-level accuracy, the NEO-M9L is integrated with dead-reckoning technology and sensor fusion, using algorithms that process sensor data from the IMU and from wheel-speed sensors. It can provide reliable location data in challenging environments, such as urban canyons, where multipath becomes an issue, or tunnels, where GNSS signals are partially or totally denied, Steinhauser said. Additionally, the NEO-M9L can operate in temperatures of up to 105° C, making it suitable for integration on the roof, behind the windscreen, or inside hot electronic control units. The NEO-M9L addresses the use cases in urban environments for both navigation and systems, such as Europe’s eCall, that provide an automated message to emergency services following a road crash, including the precise location of the accident.
The ZED-F9K, on the other hand, is well suited for use cases at the higher levels of advanced driver assist systems (ADAS) defined by the Society of Automotive Engineers (SAE), which require decimeter-level accuracy. “At L3 and above, you need correction services with integrity to allow for trustworthy and reliable GNSS positioning,” Steinhauser said. “We have partnerships with Bosch on projects to develop functionally safe GNSS solutions based on a ISO26262-certified version of u-blox generation 9 GNSS technology.” The ZED-F9K is a multi-band receiver that uses GPS signals on L1-L2 and Galileo signals on E5b. “We also have a special set of features adequate for the ADAS and the autonomous driving features,” Athmanathan said.
Image: 3alexd/E+/Getty Images
Bottlenecks
One of the factors limiting how quickly u-blox can roll out solutions based on the ISO 26262 standard (titled “Road vehicles – Functional safety”) is that highly autonomous systems require more integration work by the customers, said Alex Ngi, the company’s product manager for High Precision GNSS. “The first systems are now available.” Another hurdle, he pointed out, is the legal framework for deploying autonomous driving systems. “The regulations about how things need to be tested, and the liabilities for when systems fail, affect how quickly these systems can get adopted.”
GNSS can be used as a complementary technology to enable absolute positioning for systems that fuse data streams from cameras and lidars, such as those used for ADAS level 2 applications. “Fusing all this is computationally intensive and requires high processing power, such as NVIDIA GPUs, which tend to be very hot systems. We see a lot of requirements for very high-temperature GNSS receivers, because our receivers are often co-located with these hot systems.”
Of course, u-blox does not simply hand its modules to Bosch and car manufacturers and say, “You take it from here.” Design and integration is an iterative process. “We bring in the GNSS know-how and integration support and Bosch brings in the functional safe automotive development know-how,” Ngi said.
Dead Reckoning and Map Matching
For the automotive market, u-blox has more than 20 years of experience with dead reckoning. “The sensor-fusion solution receives data from both the GNSS and the IMU, and we provide the complete final solution,” Athmanathan explained.
The system also aids the receiver by providing it external map data. “If you’re driving your car northbound and the GNSS receiver tells you that it’s headed in the opposite direction, or that you’ve jumped over to the lane to the other side of the highway, clearly that cannot be right,” Ngi said. “Map matching relies on simple messages that come into our receivers to give us positive feedback on our measurements.”
For non-automotive applications, u-blox makes the ZED-F9R. It is used, for example, in robotic lawnmowers, very common in Asia and Europe, which require centimeter-level accuracies. “That’s why it focuses on delivering corrections using SPARTN, which can be a continent-wide data stream,” Ngi said. “We also make the design so that it’s very easy to integrate and enables the designers to easily pass the corrections to their receivers fully encrypted. This way, the value of the data is delivered to the lawnmower without exposing it to the system designer, so that we don’t need to go check every design to see whether somebody is leaking secured correction services.”
By the end of November, according to u-blox, updates of the ZED-F9P multi-band GNSS receiver will include decryption of the SPARTN correction data and a 95-percentile protection level. The protection level increases the trust non-safety-critical applications can place in its position output. By continuously outputting the upper bound of the maximum likely positioning error, referred to as the protection level, the receiver lets autonomous applications, such as UAVs or robotic lawnmowers, make efficient real time path planning, increasing the quality of their operations.
Guiding eScooters and EVs
In some places, Ngi pointed out, e-scooters are required to use a bike lane, which might be only two or three feet wide and may not be along the side of a building as it would be on a sidewalk. “The ZED-F9R is a much more flexible solution than camera systems that only know sidewalks or bike lanes.” Bird uses it to throttle driving speeds to match speed limits, which change from one location to another. “It is also much more scalable for them as opposed to such solutions as using UWB [ultra-wideband] beacons to fence off different areas, which are not really scalable for a company that wants to deploy solutions to hundreds of cities.”
Xpeng Motors, a manufacturer of smart electric vehicles, uses u-blox F9 GNSS receivers, which use signals from all four GNSS constellations, in its P7 super-long-range sports electric vehicle sedan. The vehicle uses ADAS for navigation-guided driving, automated parking and autonomous driving. For instance, once a navigation destination is set on a specific highway, the P7 will follow the route guidance to execute autonomous lane changing, switch to high-speed routes, and select the optimal route in real-time.
In April, the pizza company Domino’s and self-driving delivery company Nuro launched autonomous pizza delivery in Houston, Texas. Select customers who place a prepaid online order on certain days and times from Domino’s in Woodland Heights can choose to have their pizza delivered by Nuro’s R2 autonomous, occupantless on-road delivery vehicle.
Customers selected for the service receive text alerts, which update them on R2’s location and provide them with a unique PIN to retrieve their order. Once R2 arrives, customers are prompted to enter their PIN on a touchscreen, opening its doors.
In February 2020, Nuro became the first autonomous vehicle developer to be given exemptions by the U.S. National Highway Traffic Safety Administration for testing on public roads without the need to have controls for human operators. Unlike many other autonomous vehicle companies, Nuro engineered its self-driving road vehicles to transport goods instead of people.
There’s no set timetable for how quickly Domino’s and Nuro will evaluate their testing or expand the service.
Nuro is also carrying out trials and pilot deliveries with several other companies, including restaurant chain Chipotle, Kroger grocery stores, CVS pharmacies, Walmart and FedEx.
Scooter company Bird and u-blox have jointly developed a new Smart Sidewalk Protection system to help prevent shared scooters from operating on city sidewalks. It uses the u-blox ZED-F9R, a dead-reckoning module that fuses GNSS and sensor data, delivering centimeter-level location information in any condition. This allows the system to monitor whether a Bird e-scooter is being operated unsafely, such as on a sidewalk or speeding. Using Bird data, the companies co-developed a version of the ZED F9R module tailored to meet the needs of the shared micromobility industry.
The dual-band ZED-F9R GNSS receiver supports up to eight times more satellite signal types and four times more constellations (GPS, Galileo, GLONASS and BeiDou) than typical solutions. The module processes real-time vehicle data, including wheel speed, IMU sensor data (including acceleration and heading), and real-time kinematic data that corrects for ionospheric interference. The technology is also optimized for e-scooters by applying dynamic models matching their movements.
To turn this sensor-fusion module into its Smart Sidewalk Protection system, Bird developed a five-step process for creating sidewalk maps with centimeter accuracy. It starts with a geofence outline constructed from satellite imagery or city GIS data. Bird then uses surveying equipment to measure the location of three city landmarks. Only a few measurements are needed for each city. Once the landmarks have been identified, they compare their location to the satellite imagery to determine offsets and rotations and use them to shift and transform each of the original geofence outlines. Finally, they pre-load the updated geofence outlines onto Bird vehicles to eliminate latency. When combined with the hyper-accurate location measurements provided by Bird’s sensor-fusion module, they can detect and respond to sidewalk riding almost instantly, according to Bird.
The micromobility module is being piloted in Milwaukee and San Diego. Madrid will be Bird’s first pilot city in Europe, with plans for a broader roll-out slated in 2022.
