SoftBank plans to introduce a centimeter-accurate, real-time satnav-based positioning service, specifically using Japan’s Quasi-Zenith Satellite System (QZSS), to guide autonomous vehicles across a range of industries in Japan. The company said it will install more than 3,300 control points at base stations across Japan to deliver centimeter-level accuracy over its mobile network coverage area to provide real-time kinematic (RTK) positioning.
Testing begins in July with a scheduled launch of commercial service by the end of November. Test partners include Yanmar Agribusiness Co., Ltd., a provider of autonomous assisted driving for agricultural machinery, Kajima Corporation, which performs construction site management with automatically controlled drones for aerial photography and monitoring, and SB Drive Corp., a provider of autonomous and assisted driving technology for buses.
SoftBank is developing proprietary low-cost GNSS receivers so that “new services and market expansion can be realized.” A Positioning Core System provided by ALES Corp. will generate correctional data based on signals received and transmitted by SoftBank’s own control points over SoftBank’s mobile communications network to agricultural and construction machinery, self-driving cars, drones and other equipment carrying GNSS receivers. The company expects that centimeter-level positioning can thus be done in real time.
In addition to control points at its own base stations, SoftBank will use the Geospatial Information Authority of Japan’s approximately 1,300 GPS-based control stations.
SoftBank is also developing services to enablec loud-based RTK positioning for devices without GNSS receivers. Cloud-based RTK will provide centimeter-level, location-based services for equipment that needs to be miniature and energy-efficient, such as infrastructure surveillance sensors and wearable devices.
SoftBank Group Corp. is a Japanese multinational conglomerate holding company headquartered in Tokyo. It owns operations in broadband, fixed-line telecommunications, e-commerce, internet, technology services, finance, semiconductor design and more. It is the 36th largest public company in the world, and the 2nd largest in Japan.
ALES is a joint venture established by SoftBank and Enabler in July 2018. Enabler employs GNSS and related technologies to produce such products/services as a synchronization solution for mobile base stations for subway stations and a patented indoor positioning/time synchronization infrastructure platform in Japan.
Allystar Technology Co. Ltd., headquartered in Shenzhen, China, has released a multi-band multi-GNSS chipset, the HD9310. The new product is based on the Cynosure III architecture integrating multi-band multi-system GNSS RF and baseband.
A multi-band, multi-system system-on-chip, it supports BeiDou-3 and is capable of tracking all global civil navigation systems (GPS, BeiDou, Galileo, GLONASS, IRNSS, QZSS and SBAS) in all bands (L1, L2, L5, L6), said Simon Sun, Allystar general manager.
Photo: Allystar Technology
Designed for high-precision applications, the HD9310 measures 5.0mm x 5.0mm. The architecture integrates floating-point arithmetic units based on ARM CortexM4, 160 KB RAM, 32KB backup RAM with VBAT, 386 KB embedded FLASH and peripheral interfaces UART, I2C, SPI, GPIO, CAN.
In terms of the manufacturing processes, it adopts a 40nm process and incorporates a variety of advanced design technologies, endowing it with very power consumption: less than 50mA.
The quad-flat no-leads package allows customers to reduce printed circuit board and bill of materials costs while reducing the number of peripheral devices. This chip supports CAN interface and can be widely used in vehicle management, car navigation, wearable devices, GIS data collection, precision agriculture, smart logistics, driverless, engineering survey and other fields.
“The HD9310 supports three options of RF setting — A, B, C — for product developers to quickly bring their ideas to the different application and markets,” added Shi Xian Yang, high precision project manager at Allystar.
Three available options for the HD9310 chipset. Graphic: Allystar Technology
Option A, focused on L5 band, L5/E5, maximizes measurement accuracy and improves multipath mitigation based on higher chip rate.
Option B is focused on L2 band, and suitable for relative position applications, for example, real-time kinematic (RTK), because worldwide continuously operating reference stations (CORS) commonly support L1/L2/L1OF/L2OF.
Option C is focused on the L6 band and is designed for PPP applications, receiving state space representation (SSR)-type corrections to be broadcast from satellites in the coming future, and supporting B3I already.
The HD9310 comes with built-in support for standard RTCM Protocol (MSM), supporting multi-band multi-system high-precision raw data output, including pseudo range, phase range, Doppler, SNR for any kind of 3rd party integration and application.
Module. Allystar Technology also has launched a multi-band multi-GNSS module, TAU1302, which integrates the HD9310 chipset and measures 12 × 16 × 2.3 millimeters.
With the features of small size, low power consumption (<50 mA), and ease of integration and mass production, HD9310 is suitable for high-precision applications such as vehicle management, car navigation, wearable devices, GIS data collection, precision agriculture, smart logistics, driverless, engineering survey and other fields.
Customer samples of the HD9310 chipset are available now.
“As of today [Nov. 28] Tesla owners have driven 1 billion miles with Autopilot engaged,” the company announced via tweet.
The Autopilot feature became available in 2015 and now comes on all new Tesla models with a $5,000 activation fee at the time of purchase or $7,000 if selected later.
The company is training its “neural networks” to improve its self-driving system.
Photo: Tesla
Tesla’s global fleet totals more than half a million vehicles, and recently marked a 20-billion mile step of total electric miles driven, the company said.
The Autopilot system can also function in the background of the vehicle, without being activated and with no input on control. Thus it gathers data from many more billions of “drivered” miles about its environment and potential Autopilot behavior.
The company previously mentioned the 1 billion-mile autonomous mark as the minimum it would need to move Autosteer from beta to a regular feature.
Updates to Autopilot are planned for 2019, including new hardware that will aid in the rollout of the company’s Full Self-Driving system, possibly by the end of that year.
The U.S. Department of Transportation’s most recent document preparing for the future of self-driving cars almost entirely ignores positioning, navigation and timing (PNT) needs, according to the Resilient Navigation and Timing (RNT) Foundation. And when it does address GPS, it gets things wrong. A Dec. 3 deadline looms for interested parties to file their comments with DOT.
The comment period is open until December 3. Interested parties can make their own comments and read those already submitted at the website for Docket DOT-OST-2018-0149.
The cited comment from the RNT Foundation states that, while most self-driving cars are being designed to navigate without external inputs, GPS/GNSS will still be required to initialize location information for vehicle cold startups. Also, most vehicles will reference GPS/GNSS when communicating their positions to other vehicles and traffic control systems.
Much of the benefit of automated vehicles will come from their participation in Intelligent Transportations Systems. This means wireless networks. The RNT Foundation also urges the department to consider these networks’ critical dependence on GPS timing synchronization in their plans going forward.
(Image: Pavel Vinnik/Shutterstock.com)
The Secretary of Transportation has had a mandate to provide a backup capability for GPS since 2004 that has not been acted upon. The RNT Foundation comments observe that doing so could greatly mitigate all of the concerns mentioned.
Dana Goward is president of the Resilient Navigation and Timing Foundation, based in Washington D.C.
Representatives from the global automotive industry gathered at the the Intelligent Transport Systems (ITS) World Congress in Copenhagen in September. At a “Galileo for Mobility” session, panelists showed off new products and discussed the benefits of GNSS for the deployment of multimodality, new mobility services and digital platforms by transport authorities, industries and users.
Their goal: to make safe driverless road transport a reality.
Autonomous driving with multi-GNSS
Cover image of Galileo for Mobility leaflet. (Image: GSA)
Germany’s ANavS GmbH provides position and attitude solutions with centimetre-level accuracy. Fast fixing is achieved by using three GNSS constellations and the company’s patented RTK fixing technology. The system combines multi-GNSS (GPS + GLONASS + Galileo), inertial sensors, vehicle data, visual odometry and feature mapping, as well as LiDAR and radar. Tight coupling of GNSS and all of these other systems ensure reliable positioning even in areas with limited satellite visibility.
ANavS managing director Patrick Henkel said, “Our sensor fusion framework delivers precise position and attitude information for navigation. It also generates real-time, highly accurate maps with high resolution. The platform can be used for the whole range of transport applications from road transport to maritime and drone navigation, as well as in robotics, surveying applications and of course in agriculture for precision farming.”
The system is particularly well suited to autonomous driving applications because of its high accuracy, high availability and continuity, and, with Galileo, its integrity, according to Henkel. The ANavS module is available in different versions, with one, two or three integrated GNSS receivers, depending on the level of performance required.
Sensor fusion with non-connected vehicles
Swedish truck manufacturer Scania led work on the EU-funded project, Precise and Robust Positioning for Automated Road Transports (PRoPART), demonstrating a high-availability positioning solution for connected automated driving applications. The system implements sensor fusion using information from both the on-board vehicle sensors and an off-board road infrastructure traffic sensor, accounting also for non-automated and non-connected road vehicles.
“We are benefiting from the high multipath mitigation enabled by the Galileo binary offset code, and there is a substantial improvement of reliability of the carrier phase ambiguity resolution,” said senior engineer Fredrik Hoxell. “All of this makes Galileo a really good addition to our sensor platform,” he said.
Big data contribution
Digital mapping is of course a critical resource for autonomous driving applications, and Tom Jensen of the veteran manufacturer of personal navigation devices TomTom stated “We have been compiling data from our GNSS receiver users for 10 years. We have 500 million devices currently running and today we have about 90 trillion data points!”
TomTom has dedicated itself to fusing that data for the generation of detailed maps that can be updated within minutes, for understanding traffic flow and traffic changes in near real time. “Now we want to open that up for the users,” he said. “We are meeting with public authorities, governments, decision makers who we know can use this information, for the roads, for the infrastructure, to plan their projects in the best and most intelligent way.”
Preventing terrorist attacks
The H2020-funded TransSec project coordinated by Daimler AG Trucks targets a solution to the recent rise in vehicle-based terror attacks across Europe, often employing heavy trucks to attack pedestrians.
Oihana Otaeguim, head of ITS at TransSec project partner Vicomtech, said, “We are developing and evaluating autonomous systems to detect and prevent trucks from being misused, to prevent these incidents from occurring. The trustability provided by Galileo is very remarkable. We have achieved advances in GNSS positioning, map data and map matching. On-board environment sensors and V2X communication are all combined in a local dynamic map. This can then be used for movement monitoring, critical area alarm, pre-crash object detection and for the implementation of non-defeatable emergency manoeuvres.”
The project team is also concerned with developing new and more effective methods to combat GNSS jamming and spoofing, which represent further threats to security in the context of automated driving technologies. Here, Galileo’s unique authentication feature will play an important role.
3D mapping
Japan’s Strategic Innovation Promotion Program, Automated Driving for Universal Services (SIP-adus) conducts several activities previewing the next generation of road transport systems: the human-machine interface in for autonomous and semi-autonomous driving, and the application of automated driving technologies in buses. The goal is precise stopping at bus stops with almost no space between the bus and the curb, to facilitate boarding and exiting for wheelchair users and elderly passengers.
“The project is validating the specifications and accuracy of a high-accuracy 3D mapping function,” Satoru Nakajo of the University of Tokyo said, “including data updating and distribution systems, and of the critical linkage of dynamic data delivered via road infrastructure.”
Public transport on demand. Area Metropolitana de Barcelona (AMB) will replace an existing fixed bus line with low demand with a flexible service that adapts bus routes according to the actual demand, improving the service and engaging new users without increasing public expenditure. The Galileo-based technology platform will consist of a mobile app and a system that manages requests, confirmations and cancellations, finds the best routes, and monitors distances travelled and payments.
Shared taxis. The pilot aims to alleviate Thessaloniki’s city centre congestion by reducing the number of trips from two eastern suburbs to the city. Ride sharing will be offered to commuters through 20 taxis provided by Taxiway at a flat rate.
Service aggregator. The Mobility as a Service (MaaS) app gathers mobility services available in Barcelona, Madrid and other big cities in Spain. It includes public transport, sharing services by motorbikes, bikes and cars, and bike parkings in these cities, improving accuracy and availability in urban areas, enabling a fast and smooth transition between transport modes, and offering the user a door-to-door and seamless multimodal trip experience.
Campus shuttle. The pilot will link autonomous electric vehicles to major hubs in a university or hospital campus (location to be determined).
Vehicle sharing. The Clem’ project will operate a last-mile transportation service to the community in Plateau de Saclay, an urban campus under development in the suburbs of Paris designed to welcome 85,000 students, workers and inhabitants by 2025. The pilot will include sharing a mixed fleet of 10 geolocated electric cars and 20 electric bikes.
This account drew heavily from published reports by the European GNSS Agency (GSA), available in full here.
Swift Navigation has released Skylark, a cloud-based GNSS corrections service delivering centimeter-level accuracy without deploying and maintaining a GNSS network. Skylark targets autonomy applications at scale and enables high-precision positioning for mass market automotive and autonomous vehicle applications.
Skylark works with both of Swift’s multi-band, multi-constellation GNSS receivers, the Piksi Multi and the Duro ruggedized industrial receiver. Swift added GLONASS support in its 1.4 firmware upgrade, announced earlier this month, and aims to include Galileo and BeiDou in the near future.
Previously known as a hardware company, Swift Navigation appears to be shifting its focus a bit, including an Internet-delivered service in addition to its GNSS receivers. It has recently focused more closely on the automotive sector; it also has customers in drone technology, robotics and precision agriculture.
Its new platform for high-precision GNSS navigation of autonomous vehicles, via Internet connectivity, Skylark delivers fast convergence times measured in seconds, using positioning algorithms to provide a continuous stream of data to individual devices from the cloud. The data stream allows for quick and robust positioning and high reliability and availability, even in challenging environments, according to the company.
The Skylark service offers accuracy at the centimeter level. (Image: Swift Navigation)
Critical requirements for real-time absolute localization through GNSS for the automotive sector, according to Fergus Noble, co-founder and CTO of Swift Navigation, are:
high accuracy; centimeter level
availability; fast convergence, measured in seconds
integrity
scalability to support a large vehicle population
low cost.
Internet-Delivered via Cell Network
The last two requirements are fulfilled by the cloud-based approach. He characterized Skylark as a hybrid of RTK (Real Time Kinematics) and PPP (Precise Point Positioning) approaches augmented by Swift’s intellectual property, with corrections delivered over the Internet as provided by the cellular network, which he described as “robust to outages.” Cell coverage along road networks is good, Noble asserted, and 5G applications are increasing that coverage and will further enable connected vehicles. Automotive OEMs are comfortable with the level of cell coverage for this application, according to him. There has been testing to show robustness in most rural areas, and network operators are dedicated to increasing this.
“Skylark operates like a utility,” said Noble. “It is a simple, low-cost Internet data stream that provides customers with a complete high-integrity GNSS solution. Simply supply a Swift receiver with power and Internet connectivity and get real-time corrections for highly-dynamic GNSS applications.”
To realize the Skylark service, the company hired a team of cloud-based engineering experts who had a role in building Amazon and Oracle critical infrastructure. Swift Navigation is initially launching only with its own devices, but is making the service publicly-available for any customer in any vertical requiring precise positioning. “Every car company is building in autonomous functionality,” noted Noble, making clear who the company is ultimately targeting.
Skylark is currently offered in six metropolitan markets. (Image: Swift Navigation)
Swift has been working with beta customers for more than a year and is now previewing the service to all customers in six metropolitan markets: the San Francisco Bay Area, Los Angeles, San Diego, Phoenix, Pittsburgh and Detroit. The company envisions full contiguous U.S. and ultimately global expansion. Customers in preview areas with Swift receivers can sign up for Skylark and immediately start receiving corrections.
The service maintains low bandwidth to save on data costs and is offered with a free 30 day trial and flexible pricing plans. Skylark’s pricing structure includes a monthly plan and an annual plan. Enterprise pricing is available for volume orders.
Voyage Self-Driving Car Active Service and Coming Expansion
An early beta user of the service, Voyage deploys self-driving taxis in private communities across North America. “Skylark and Piksi Multi are working safely and efficiently in a real-world application today at The Villages, a retirement community in San Jose, California,” said Oliver Cameron, co-founder & CEO of Voyage.
Voyage incorporates Skylark GNSS corrections in controlled road networks in private communities. (Image: Swift Navigation)
Voyage’s passenger cars carry a roof-racked suite of sensors: the Swift Navigation Piksi Multi GNSS receiver, LiDAR, cameras, radar, and an inertial measurement unit. A computer in the trunk integrates all sensor signals and uses the car’s CAN bus to operate steering, braking, and other functions. An operator sits behind the wheel at all times, sometimes with a co-pilot: one to watch the road ahead, and one to watch the software. “Safety is our first priority,” said Cameron.
The service is especially valuable to customers with mobility limitations that might prevent them from walking to an event or moving within the community. (Image: Swift Navigation)
The Voyage fleet stays within the bounds of a given community, where all roads have been precisely mapped, speed limits are lower and traffic patterns are more clearly defined than in metropolitan cities. The first in the San Jose area serves private community of more than 4,000 residents, with a 15-mile road network. Today, residents are able to summon a Voyage self-driving taxi using a smartphone app and have a ride waiting at their front door. This service is especially valuable to customers with mobility limitations that might prevent them from walking to an event or moving within the community. Voyage takes residents of The Villages to and from the gym, to visit with friends, to the golf course and to community center events.
Image: Swift Navigation
Voyage will next deploy the Swift product suite in its upcoming deployment launching to 160,000 retirees at The Villages complex in Florida, over a road network of 750 miles. It is currently in a “Q/A” testing phase on that site, working the technology and the local mapping through their paces.
There was plenty for a philosophy major to sink his teeth into at ION’s January workshop on Cognizant Autonomous Systems for Safety Critical Applications (CASSCA).
What is knowledge? What is meaning? What is understanding? What is intelligence? What is learning? What is thinking?
These questions excited Plato and Kant, Buddha and Descartes, perhaps out of intellectual or spiritual curiosity. Who’s to say? But the people asking them now are driven, quite literally, by practicalities. They have come to realize that we cannot ride in driverless cars or fly in pilotless plane-taxis, we cannot live in an autonomous, artificially intelligent environment without knowing a bit more exactly what knowledge is, in this brave new world.
Without thinking about what thinking may be, for a machine.
Why does this matter to a GPS/GNSS/PNT readership? Because as positioning and navigation engage more deeply with artificial intelligence (AI) generally, and with autonomy in particular, these issues emerge as part of the environment that such solutions explore, and in which they must verify and validate themselves.
Welcome to the future, it’s yours. Now think about it.
Culture Club. Some of us may have believed that only technical obstacles remain in the path of a driverless car and an otherwise automated society, salted with a few regulatory wrinkles to iron out. But as build-a-robot R&D projects transform into full commercial partnerships, cultural challenges jump up as well: inertia, instability of requirements, unanticipated expectations, magical thinking (the development of empathetic attitudes towards robots), misplaced trust and misplaced distrust. All this according to Signe Redfield, roboticist and mission manager at the U.S. Naval Research Laboratory.
Joao Hespanha, professor of electrical and computer engineering at the University of California, Santa Barbara, outlined three key concepts for AI development: computation, perception and security. The critical questions for the first named are, how much computing will be done onboard the platform, how much learning will be done onboard, and how much of each process will be distributed to offboard computation. Perception, a crux for autonomy, is closely bound in a feedback loop with control. The platform must gather data to make autonomous decisions (control), and those decisions must maximize the gathering of information (perception).
Amply consider security. All safety-critical systems must provide for — and prevent where possible — decisions based on compromised measurements, which may stem from system or environmnetal noise, sensor faults, hacked sensors, or other corruptions.
Second Wave. We are in the second wave of AI, according to Steven Rogers, senior scientist for sensor fusion at the Air Force Research Laboratory. In the first wave, 60s and 70s, large and complex algorithms, relatively low on data, drove new developments — but they hit real-world problems, hard. Since the mid-80s, we have been in the “classify” stage with relatively simpler programs generating and consuming lots of data. Intense statistical learning will eventually lead to the third wave of AI: Explain.
On a timeline yet to be determined, contextual adaptation will give rise to “explainable” AI, capable of answering unexpected queries. That is, it will have learned how to teach itself.
Some of this stuff gets pretty scary.
Most future knowledge will be machine-generated.
Let’s run through that one more time.
“Most future knowledge on Earth will come from machines extracting it from the environment,” said Rogers. “Machine generation of knowledge is key for autonomy.”
Here’s where the thought processes really started to levitate. “Current sense-making solutions are not keeping pace, not growing as knowledge is growing,” Rogers asserted. And he challenged us with the questions posed at the beginning of this column: in AI, the context we will use to explore much of the future, what is knowledge? What is meaning? And so on.
He gave us one of his answers: “Knowledge is what is used to generate the meaning of the observable for an autonomous system. Correspondingly, machine-generated knowledge is what is used to turn observables into machine-generated meaning.”
Slide from Steven “Cap” Rogers’ presentation at CASSCA.
He suggested a book by George Lakoff and Mark Johnson, Metaphors We Live By. Pretty heady stuff for a room full of engineers. I don’t know about you. I’m headed down to the library to check it out.
Requirements, Simple/Not. We got back to earth with some technical challenges we could actually chew on with David Corman, program manager for Cyber-Physical Systems and Smart and Connected Communities at the National Science Foundation. Seemingly simple requirements for safety-critical applications break down into hundreds of requirements that no one has really thought about, Corman said, as he displayed a chart of “Some Example Research Problems.”
Precision agriculture and environmental monitoring are two sectors where he thought autonomous operations come closest to being full realization, because their operational environments are structurally defined enough. In such constrained niches that we more fully understand, we can implement autonomous operations. Elsewhere, “we don’t know how to specify what we want, so that we get only ‘good results’ and no ‘bad results.’ ”
He identified a looming Cambrian explosion in AI, analogous to that for plants and animas following the dinosaur extinction, in which systems interact, gather data, sense the environment, learn, improve and multiply. He suggested we browse “The Seven Deadly Sins of Predicting the Future of AI,” an essay by Rodney Brooks.
The afternoon’s workshop talks followed, from experts in autonomous flight software, legal and insurance aspects of autonomy, the Ohio State University’s Center for Automotive Research, and the U.S. Department of Transportation. But I tell you, this morning done my brain in.
Before folding up, I must mention a short video on autonomous flying taxis displayed by Paul DeBitetto, VP of software engineering at Top Flight Technologies. It depicts Pop.Up, a modular ground and air passenger vehicle for megacities of the future. Check it out.
The CASSCA workshop was organized and moderated by Zak Kassas, an assistant professor at the University of California, Riverside and director of the Autonomous Systems Perception, Intelligence & Navigation (ASPIN) Laboratory. He is also co-author of two cover stories in GPS World, “LTE cellular steers UAV” and “Opportunity for Accuracy.”
ION president John Raquet expressed the hope that we may see a fully fledged conference on this topic in the near future: CASSCA 2019, perhaps, to join the rotating repertory of ION annual meetings.
Agreed. We need to think more.
Don’t look back, the machines may be gaining on us.
A free Cognizant Autonomous Systems for Safety Critical Applications (CASSCA) Workshop will be held 8:30 a.m.-5:30 p.m. on Jan. 29 at the Hyatt Regency Reston in Reston, Virginia. This is scheduled to take place the day before and at the same location as the Institute of Navigation’s International Technical Meeting and Precise Time & Time Interval Systems and Applications (ION ITM/PTTI) 2018 conference.
The workshop consists of a full day of presentations and discussions on the opportunities and challenges associated with developing fully autonomous systems that are cognizant and trustworthy for safety-critical applications by leading experts in the field. Speakers include:
David Corman, Program Manager, Cyber-Physical Systems Program; National Science Foundation (NSF). Dr. Corman’s current research interests are in the field of Cyber Physical Systems (CPS), security for CPS, unmanned systems, manufacturing, and technologies supporting Smart and Connected Communities.
Paul DeBitetto, Vice President, Software Engineering; Top Flight Technologies. Dr. DeBitetto leads all Top Flight’s Software and Embedded Systems Development. That includes product-related flight control, simulation, computing, sensing, data communications, security-related controls and software solutions.
Finch Fulton, Deputy Assistant Secretary for Transportation Policy; Department of Transportation (DOT)
Joao Hespanha, Professor and Chair of Department of Electrical and Computer Engineering; University of California, Santa Barbara. His current research interests include hybrid and switched systems; multi-agent control systems; distributed control over communication networks (also known as networked control systems); the use of vision in feedback control; stochastic modeling in biology; and network security.
Robert Peterson, Professor and Director of Center for Insurance Law and Regulation; Santa Clara University.
Mr. Peterson teaches torts, insurance law and regulation, evidence and products liability, and other courses. He is a past chair of the California State Bar Standing Committee on Insurance Law, and is director of the Law School’s Center for Insurance Law and Regulation.
Signe Redfield, Roboticist and Mission Manager; Naval Research Laboratory (NRL). Dr. Redfield’s primary interests include performance evaluation of autonomous systems, foundations of robotics, and cooperative behaviors for autonomous underwater vehicles.
Giorgio Rizzoni, Professor and Director of Center for Automotive Research (CAR); The Ohio State University. The CAR is an interdisciplinary university research center that conducts research on advanced automotive and transportation technologies and systems engineering, focusing on sustainable mobility, advanced propulsion systems, human safety and the environment.
Steven Rogers, Senior Scientist for Automatic Target Recognition and Sensor Fusion; Air Force Research Laboratory (AFRL). Dr. Rogers serves as the principal scientific authority and independent researcher in the field of multi-sensor automatic target recognition and sensor fusion.
Registration is also now open for the ION International Technical Meeting (ITM) and Precise Time and Time Interval Systems Applications Meeting (PTTI), which begin the next day (January 30-February 1) at the same location. See www.ion.org/itm for more information.
Enabling the future of autonomous transportation by significantly reducing product development time is the shared goal of three presentations to be made on Thursday, Nov. 30 in a free webinar, “High Accuracy for Autonomous Driving.”
The speakers will show how they employ post-processing software to generate accurate and reliable ground reference solutions in vehicle testing. The software enables evaluating potential sensor suites, benchmarking solutions, and generating high-definition maps.
Post-processing the data from autonomous vehicle tests under varying environmental conditions that mirror real-world situations can mitigate GNSS error sources (satellite clock & orbital error, and ionospheric & tropospheric delay); establish an ultra-precise ground truth reference for testing; compare and contrast different sensor packages tested onboard the vehicle; produce customized data formats for exporting information; compare real-time and post-processed quality; transform and translate data between different locations and reference frames; and revisit tests through export to Google Earth. The speakers will show how post-processing forward and back can lead to as much as 40 percent data accuracy improvement.
The software package, Inertial Explorer, offers this capability, whether lower-grade or high-end inertial sensors are employed.
Steven Waslander, associate professor at the University of Waterloo, heads a project collecting 1,000 km of data in all-weather conditions for a new public road driving dataset focused on autonomous driving challenges. He directs the Waterloo Autonomous Vehicle Laboratory (WAVELab), extending the state of the art in autonomous drones and autonomous driving through advances in localization and mapping, object detection and tracking, integrated planning and control methods and multi-robot coordination.
Terry Lamprecht, director of products at AutonomouStuff, a supplier of components, services and software that enable autonomy, will discuss verifying proper installation, and creating a baseline data set to benchmark against data collected on autonomous vehicles in real-time.
Natasha Wong Ken, product manager at Waypoint, will give a high-level technical overview of post-processing techniques and settings, including forward and reverse processing, tightly vs. loosely coupled, PPP vs. differential, and more.
Registration for the November 30 webinar is free. For those not able to attend the live broadcast, all audio and presentation slide components can be downloaded after air date for viewing at convenience.
All poll takers will be entered in a drawing for a $50 gift card.
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Meanwhile, here’s a preview of the V2V Countdown article from the May issue, introduced by Chaminda Basnyake, an engineer at Locata Corporation:
The U.S. Department of Transportation (USDOT) released a Notice of Proposed Rulemaking (NPRM) in December 2016 for the deployment of Dedicated Short Range Communications (DSRC)-based vehicle-to-vehicle (V2V) safety applications as part of the connected vehicles (CV) and automated vehicles (AV) initiative. If all goes well, this mean a V2V deployment mandate for new passenger vehicles likely starting in 2021 and reaching all new vehicles within 2–3 years.
Standards required for V2V deployment were published in 2016 or before, including the V2V Minimum Performance Requirements SAE 2945/1, leading the way for commercial product development. The USDOT, which has been the catalyst behind V2V industry R&D starting from the automaker collaboration CAMP (Crash Avoidance Metrix Partnership) in 2001, is conducting CV Pilot programs in New York, Wyoming and Florida. These offer the opportunity for state DOTs, vendors and all other stakeholders to test the technology in real-life scenarios.
Automotive OEMs have been developing this technology for more than a decade, and the NPRM is the beginning of a race toward integrating V2V to production vehicles. Deploying V2V technology requires the close cooperation of OEMs, their suppliers and many other stakeholders.
This article captures the views of major players in the CV marketplace on expected deployment timelines, remaining challenges such as reliable positioning technology, integration with existing systems, and the implications on AV technology.
The May issue of GPS World carries these three expert opinions on the question: How are autonomous vehicles and V2V technologies driving innovation within the GNSS industry?
Chaminda Basnyake
Chaminda Basnyake
Principal Engineer, Market Development,
Locata Corporation
We still have technical and cost versus performance challenges to meet the PNT needs of V2V and AV. Positioning and even timing expectations in deep urban areas are still not met reliably. As a result, ad hoc methods such as HD map-based nav — methods that work but are not scalable — have emerged. Innovations to deal with multipath, signal visibility and geometry are critical. Solutions that enable real-time mapping will be essential for scalable AV deployment.
Curtis Hay
Curtis Hay
Technical Fellow, GPS & Maps,
General Motors
Four key areas the commercial GNSS industry is pursuing include: low-cost, high-volume dual-frequency chipsets; broadly available PPP and network RTK corrections delivered either through mobile IP or satellite; precise maps for highways, urban centers and trunk roads that achieve 10-cm localization relative to WGS-84; and improved integrity monitoring and fault detection. The National Highway Transportation and Safety Administration also released a proposed rule-making with tight standards for GNSS performance: 1.5 meters, 1-sigma confidence.
Jonathan Auld
Jonathan Auld
Director, Safety Critical Systems,
NovAtel
Unlike traditional GNSS applications, automotive positioning requires high-precision accuracy at extremely low cost and size. Most importantly, this performance must be achieved with high reliability while operating in the toughest environments. Solving this positioning challenge is driving innovation in the system engineering of multi-frequency receivers and antennas along with extending performance through sensor fusion with lower cost devices. Additionally, there is significant work in the area of safety and integrity for land-based applications.
Here’s a preview of the V2V countdown article from the May issue, introduced by Chaminda Basnyake, an engineer at Locata Corporation:
The U.S. Department of Transportation (USDOT) released a Notice of Proposed Rulemaking (NPRM) in December 2016 for the deployment of Dedicated Short Range Communications (DSRC)-based vehicle-to-vehicle (V2V) safety applications as part of the connected vehicles (CV) and automated vehicles (AV) initiative. If all goes well, this mean a V2V deployment mandate for new passenger vehicles likely starting in 2021 and reaching all new vehicles within 2–3 years.
Standards required for V2V deployment were published in 2016 or before, including the V2V Minimum Performance Requirements SAE 2945/1, leading the way for commercial product development. The USDOT, which has been the catalyst behind V2V industry R&D starting from the automaker collaboration CAMP (Crash Avoidance Metrix Partnership) in 2001, is conducting CV Pilot programs in New York, Wyoming and Florida. These offer the opportunity for state DOTs, vendors and all other stakeholders to test the technology in real-life scenarios.
Automotive OEMs have been developing this technology for more than a decade, and the NPRM is the beginning of a race toward integrating V2V to production vehicles. Deploying V2V technology requires the close cooperation of OEMs, their suppliers and many other stakeholders.
This article captures the views of major players in the CV marketplace on expected deployment timelines, remaining challenges such as reliable positioning technology, integration with existing systems, and the implications on AV technology.
As manufacturers convert machines and appliances into remotely controllable objects (the Internet of Things), the potential for spoofing expands, perhaps exponentially. Hackers could interfere with the data supplied to autonomous cars or tracks, remotely forcing them to crash.
Although the dangers of GPS spoofing have been pointedly discussed in may technical papers and articles in GPS World since the early 2000s, manufacturers have not devoted much attention to them because there weren’t many devices making use of location-based technologies, according to associate professor Dinesh Manandhar of the University of Tokyo.
With the proliferation of GPS-capable smartphones and other networked devices, “anyone can become a target of the attack,” Manandhar told the Japan Times in a recent interview.
“Too many things today use GPS as a reliable source of location information,” Manandhar said. “People trust the location information from GPS satellites like God. When PCs became common for many people, the sudden outbreak of computer viruses became an issue around the world, and anti-virus software become an essential tool for everyone to protect their data,” he added. “The same thing is now happening around GPS. We need a system to fight back against the risk.”
Manandhar cited some possible examples of spoofing, both by consumers — “You can falsify your smartphone’s information and make it look like you are going back and forth between Tokyo and Hawaii within just three minutes,” and by sophisticated criminals. “Let’s say I were a top manager of a major bank. I could access all the information while sitting at my desk, but I wouldn’t be able to access it from the room next to it. But people could get access to such information if they disguised the location information received by computer.”
Manandhar and many other researchers around the world are developing and testing anti-spoofing techniques, but it is a long step from demonstrated results to integration into products reaching market. “The products we are designing today are ones that we will use five years later. So we must assume the possible risks and prepare for the threats that might jeopardize our society in the future.”
How about 2032? 2035 or after?
