One of the specific objectives of the new scheme is to improve incentives for transitioning the country’s HGV fleet towards lower emission vehicles. It will also help reduce the impact these vehicles have on infrastructure and road wear costs, as well as their noise impact.

The project is being managed by Sund & Bælt, a Danish government-owned company that manages the Storebælt and Øresund road links. The company will be responsible for both implementation and operation of the new system. A GNSS onboard unit (OBU), costing around €150 including installation, will be mandatory for HGV operators, with a cheaper, self-installed OBU option also planned. Manual or pre-paid ticket options will likely not be available.

No standing still

A first step in the procurement process has already been initiated and will continue until early 2024. Sund & Bælt have engaged in talks with a number of suppliers with experience in the delivery and maintenance of GNSS and distance-based tolling solutions. Key technical challenges include GNSS data handling, map and toll context data and map-matching, segment identification and toll calculation. A testing phase is taking place in early 2023, with final commissioning set for early 2025. Under the current Eurovignette scheme, HGV operators have to buy a small electronic device if they want to use motorways and toll highways in the Eurovignette countries, which include Denmark, Luxemburg, the Netherlands and Sweden. Eurovignette’s revenues are currently around €67 million per year. The Danish government expects the new GNSS-based scheme to match that until 2027, and then to double it from 2028 onwards.

The new scheme leverages significant technological advancements as well as increasing market maturity of GNSS telematics OBUs, mobile communications and enforcement equipment, all occurring during the past decade. The costs of establishing and operating the system will be comparable to those of previous non-GNSS-based schemes.

The post Denmark Planning New GNSS-Based Road User Charging Scheme appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

There are many good reasons for getting excited about highly automated vehicles, or HAVs, which is the acronym used by the National Highway Traffic Safety Administration (NHTSA). HAVs can make driving more fuel- and time-efficient. They can significantly reduce traffic congestion and emissions by driving a precise speed, minimizing lane changes, and maintaining an exact distance to neighboring cars. They can also increase accessibility and mobility for disabled and elderly persons.

There are many good reasons for getting excited about highly automated vehicles, or HAVs, which is the acronym used by the National Highway Traffic Safety Administration (NHTSA). HAVs can make driving more fuel- and time-efficient. They can significantly reduce traffic congestion and emissions by driving a precise speed, minimizing lane changes, and maintaining an exact distance to neighboring cars. They can also increase accessibility and mobility for disabled and elderly persons.

Sharing an HAV instead of owning is projected to dramatically reduce a household’s yearly transportation budget, which currently ranges between approximately $8,000 and $11,000 per car. HAVs carry promises not only in improved road mobility, and accessibility, but also in producing architectural and societal changes that can make mass parking spaces and personal car ownership obsolete in urban areas. Above all, HAVs can help improve road safety by preventing car accidents that cause more than 30,000 deaths/year in the United States alone, cost approximately $230 billion/year in medical and work loss costs, and are caused by humans 90% of the time.

Press articles in the 1950s and 1960s predicted that autonomous cars and “electronic highways” would become widely available by 1975. Major milestones in the use of new sensor, computation, and communication technology have recently reenergized the eagerness for HAVs. This first started with the 2005 “DARPA Grand Challenge”, where four different HAVs designed by teams of engineers from industry and academia completed a 132-mile trip across the Mohave desert in less than 7.5 hours with no human intervention. The 2007 DARPA “Urban Challenge” saw six teams autonomously complete a 60-mile course in an urban environment, while following traffic laws. Most teams used a combination of LiDAR, cameras, differential GPS, and computation power that is multiple orders of magnitude higher than what is typically needed for a commercial passenger vehicle. In 2009, Google (now Waymo) began designing and testing “self-driving” cars, which have since accumulated more than three million miles in autonomous mode.

Currently, most car manufacturers have HAV prototype systems and Google, Uber, NuTonomy have HAV pilot testing programs, including fully autonomous systems for public transportation, which, for now, are confined to segregated lanes and geo-fenced areas. Multiple Tier-2 supplier companies have emerged, which specialize in autonomous car technology. In early 2017, 36 companies were registered to test prototype HAV systems on public roads in the state of California.

However, in Figure 1 (for all figures, see inset photo, above right), Gartner’s “2016 Hype Cycle for Emerging Technologies” shows that HAV technology might be at the “peak of inflated expectations”, approaching the “trough of disillusionment”. Hype cycle curves are non-scientific tools that have been empirically verified for multiple example technologies over many years. Two example emerging technologies, commercial unmanned aircraft systems (UAS) and virtual reality, are included in Figure 1 for illustration purposes. The curve’s time scale may differ for each technology. One of many indicators of decreasing expectations on HAVs include a reduction in press coverage and the emergence of first negative news stories, in particular following the May 2016 crash of a Tesla Model S whose autopilot failed to distinguish a white trailer truck from the bright Florida sky. The Model S ran under the trailer causing its roof to be torn off and the operator to lose its life. The car kept going full speed on the side of the road through two fences until it hit a pole and came to a stop.

In parallel, until the end of 2016, Google was providing detailed reports of their self-driving car performance, which were designed to operate in real-world urban environments. These reports contain records of millions of miles driven autonomously, but also acknowledge “disengagements”, i.e., where the operator needed to take over control to avoid collisions. The data shows that HAVs are much more likely to be involved in collisions, even though these collisions are often of lower severity than in conventional human driving [HAVs typically get rear-ended because of their unusual road behavior] (see B. Schoettle, and M. Sivak, “A Preliminary Analysis of Real-World Crashes Involving Self-Driving Vehicles,” Additional Resources). Also, Uber’s autonomous taxis in Pittsburg have a reported rate of one disengagement per mile autonomously driven.

Moreover, the first fielded autonomous systems have revealed new safety threats. In particular, the technology’s functionality, as perceived by the human operator, does not always match the intended operational domain: for example, there have been cases of highway autopilots being used in urban areas and passing red lights without slowing down. In addition, human-machine interaction is at the heart of role confusion (is the operator or the HAV in charge?) of mode confusion (is the HAV in autonomous or manual mode?) and of the operator’s trust in this multimodal system. Misinterpretation may grow even wilder because a given functionality will not achieve the same level of performance across models and manufacturers, and operators may not be aware of the systems’ independently verified safety ratings. And, within the next few years, operators will be expected to anticipate hazardous situations and take over control. Thus, operating an HAV may require more education and different training than driving a car manually.

Current Safety Assessment Efforts
To focus this article, first consider the Society of Automotive Engineer (SAE) International’s classification of driving autonomy levels in Table 1 (see inset photo, above right). Under Levels 0 to 2, the human driver is responsible at all times, either for driving by himself, or for supervising the HAV in autonomous mode and taking control if needed. Under Levels 3 to 5, the system is self-monitoring and the driver is expected to take control, but only if requested by the system. Levels 0-4 provide partial automation under predefined driving modes and circumstances, whereas Level 5 is full autonomy.

The most advanced private car systems are currently Level 2, and pilot programs aim at achieving Level 3, although the mere presence of a kill-switch would imply that the system is actually Level 2. The transition from Level 2 to 3 is a remarkable leap that has significant implications on trust and comfort of human-machine interactions, on legal responsibility allocation between system and driver, and on technical challenges to overcome to guarantee passenger safety.

Over the past four years, the most publicized approaches to demonstrate Level 2 HAV safety have been experimental testing campaigns by Google, Tesla and Uber. Google’s approach to have HAVs drive millions of miles with minimal human intervention has been documented up until 2015. At this time, Google cars have autonomously travelled an impressive three million miles. Tesla’s autopilot is reported to have driven more than 130 million miles – on highways only – before it caused a fatality in May 2016.

In parallel, NHTSA reports about 3,000 billion miles travelled each year on U.S. highways by human drivers, with 30,000 deaths caused by traffic accidents; this corresponds to about one fatality in traffic accidents per 100 million miles driven in the U.S. But, this number accounts for incidents on all roads, in all weather conditions, and for all vehicle ages and types. Thus, a purely experimental, complete proof that HAVs match the level of safety of human driving would take about 400 years at Google’s current testing rate (of approximately 250,000 test miles per year), and would still take many decades if the testing rate increased exponentially. This is assuming that no fatalities occur during that time, that no major HAV upgrade is performed, and that the testing environment is representative of all U.S. roads. Thus, while an experimental proof is conclusive, it is not practical. Other, analytical, methods must be employed to ensure HAV safety.

Research Challenges In HAV Navigation Safety
Multiple technical aspects developed over decades for automated flying could serve as starting points for automated driving systems. Figure 2 shows research areas with overlap between aircraft (in blue) and car (in yellow) applications. Figure 2 is not intended to give a comprehensive list of all aspects of automation, but instead, it shows example technical areas that can be addressed using similar methods in aviation and automotive applications (in the green area). For example:

• performance standards set for software, communication, and electronic equipment are already being compared for aircraft versus cars in the NHTSA report by Q. D. Van Eikema Hommes, Additional Resources.
• the design of aircraft cockpit has been continuously improved over the past few decades, especially for highly-automated Unmanned Air Systems (UAS) with a remote pilot “in-the-box”; few car manufacturers envision futuristic car interiors where humans do not participate in driving, but as long as human-machine interactions are needed, lessons learned in cockpit design to avoid information overload are key. 
• while Automatic Dependent Surveillance-Broadcast (ADS-B) will be mandatory on all aircraft by 2020, a petition for proposed rule making has been issued to mandate Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) by the same date. (ADS-B is a situational awareness system for collision avoidance, through which aircraft share their positions with Air Traffic Control and with other aircraft.) 
• GNSS/INS navigation systems, which are extensively used in safety-critical aircraft navigation, are also being investigated for HAVs.
• overall safety standards also have similarities for aircraft and HAVs, which are discussed again below.

The focus of this article is on navigation safety. In aviation navigation, safety is assessed in terms of integrity (as well as accuracy, continuity, and availability, which are not discussed for brevity). Integrity is a measure of trust in sensor information: integrity risk is the probability of undetected sensor errors causing unacceptably large positioning uncertainty (See RTCA Special Committee 159, “Minimum Aviation System Performance Standards for the Local Area Augmentation System (LAAS), Additional Resources”). This top-level quantifiable performance metric is sensor- and platform-independent, and can thus be used to set certifiable requirements on individual system components to achieve and prove an overall level of safety.

The multiple separate efforts towards achieving Levels 3-to-5 HAVs reveal a compelling lack of coordination towards a common, uniform, quantifiable safety goal. Integrity can be used as an objective performance metric for open, transparent comparison and categorization across manufacturers. It can also provide a governmental regulating agency performance and testing standards for HAV certification, which would help accelerate the development, growth, and maturation of such HAVs, as displayed in Figure 3.

Moreover, the Federal Aviation Administration (FAA) has developed analytical methods to evaluate integrity. This provides the means to:

• quantify safety of existing multi-sensor systems under a variety of operating environments, thereby reducing the need for experimental testing
• allocate safety requirements to individual system components to achieve an overall target level of safety, thereby enabling design for safety 
• perform risk prediction, which is a key operational feature to enable hazard avoidance maneuvers

Several methods have been established to predict the integrity risk in GNSS-based aviation applications, which are instrumental in ensuring the safety of pilots and crew. As an example, Figure 4 illustrates a simplified definition of the integrity risk for aircraft landing applications. The aircraft positioning prediction is uncertain because of sensor measurement noise. An alert limit (AL) requirement box is represented around the predicted aircraft position. This AL is set by the certification authority, i.e., by the FAA in this application. Simply put, the risk of the actual aircraft position being outside the AL box is the integrity risk. (In practice, the most challenging part of risk prediction is to account for potentially undetected sensor faults, such as excessive GNSS satellite clock drift.)

Unfortunately, the same methods do not directly apply to HAVs, because ground vehicles operate under sky-obstructed areas where GNSS signals can be altered or blocked by buildings and trees. In general, the HAV environment is much more unpredictable than the aircraft’s, for reasons that include:

• a changing environment: traffic lights, construction, impact of rain on road adherence, sensor masking and occlusions,
• environmental diversity: intersection topography, road conditions, markings on ground, various traffic signs 
• road users that may interfere with HAV motion: other cars, trucks, pedestrians, bicyclists, etc. 
• comparatively large number of car manufacturers, equipment suppliers, and vehicle models, as well as with shorter model cycles than aircraft, causing wide variations in vehicle age and maintenance levels 
• non-uniform vehicle and road regulations at both the state and federal levels in the U.S. coupled with different international standardization processes.

Thus, HAVs require sensors in addition to GNSS, including laser scanners, radars, cameras, and odometers.

The parallel between aircraft and car applications in Figure 4 illustrates the significant challenge that lies ahead when bringing aviation safety standards to HAVs. It took decades of research and considerable resources to bring the alert limit requirement box down to 10 meters above and below the aircraft using the FAA’s GPS augmentation systems (the Wide-Area Augmentation System and the Local Area Augmentation System). For a car to stay in its lane, the alert limit requirement box must be an order of magnitude smaller, and has to maintain this level of safety in a more dynamic and unpredictable environment.

HAV Taxonomy
Creating a path to successful automated navigation requires an overall methodology to prioritize on imminently achievable objectives, and then expand to more challenging missions. First in this HAV taxonomy, a classification using six SAE autonomy levels has been presented in Table 1. This classification is further refined by segmenting a car’s trip into basic driving competencies, and by specifying the conditions under which a given HAV shall achieve these competencies. A similar classification was made in the early days of GPS-based commercial aircraft navigation safety analysis, where distinctions were made between different phases of flight, weather conditions, vehicle equipment, and airport infrastructure capabilities.

For example, in the early 1990’s, 40% of aircraft accidents were occurring during final approach and landing, and 26% during take-off and initial climb, which only represented an average of 4% and 2% of flight time, respectively. The FAA therefore concentrated their efforts on improving safety during these phases of flight. GPS augmentation systems were designed, with varying capabilities depending on airborne equipment and airport infrastructure, to guide the aircraft under the cloud ceiling, or to bring it all the way to touch-down. Similarly, the “first and last mile” are identified as the most challenging parts of HAV operations, whereas highway auto-drive systems have already been developed and implemented. In its 2016 Federal Automated Vehicles Policy, NHTSA identifies 28 HAV behavioral competencies, which are particularly challenging to meet in the first and last miles of a typical trip. These competencies are basic abilities that an HAV must have to complete nominal driving tasks; they include, for example, lane keeping, obeying traffic laws, and responding to other road users.

To better describe an HAV’s ability, the Federal Automated Vehicles Policy further specifies that basic driving competencies should be available under an HAV’s predefined Operational Design Domain (ODD), described by its geographical location, road type and condition, weather and lighting condition, vehicle speed, etc. The ODD captures the circumstances under which an HAV is supposed to operate safely.

Such classification is key to safety analysis. It can allow HAVs at different stages of their development to be simultaneously fielded, and for them to evolve by expanding their ODDs. The classification can also help in identifying geographical areas where improved road infrastructure is needed for automated operation, similar to airports requiring equipment for instrument navigation to deal with higher traffic density.

Furthermore, standards for electronic equipment, measured by Automotive Safety Integrity Levels, have been issued and compared with the aviation’s Design Assurance Levels (DAL). And, overall system safety levels have been codified, which in aviation account for both the severity and probability of occurrence of an incident, and in automotive applications account, in addition, for “controllability”, which is a measure of how likely an average driver is to maneuver out of a given imminent danger.

All of the above elements: (a) HAV autonomy level, (b) basic driving competency, (c) operation design domain, (d) vehicle electronic equipment, and (e) overall safety risk requirement must be specified to carry out a formal HAV safety analysis. Still missing from the HAV documents are clear guidelines, or example methods, on how to implement these safety requirements.

A Path Towards HAV Navigation Safety
When quantifying the safety of HAV navigation systems, such as in the example displayed in Figure 5, every component of the system including raw sensors, estimator and integrity monitor, and safety predictor, can potentially introduce risk. Unlike aircraft, HAVs require multiple and varied sensors to compensate for GPS signal blockages caused by buildings and trees. These sensor types must be integrated, and new methods to evaluate the integrity of multi-sensor systems must be developed. Furthermore, HAVs must have the ability to continuously predict integrity in a dynamic HAV environment.

In general, research on analytical evaluation of HAV navigation safety is sparse. For example, J. Lee et alia, Additional Resources use the concept of a “safe driving envelope,” but the approach focuses mostly on collision avoidance. The paper by O. Le Marchand, et alia, evaluates ground vehicle navigation, but shows an “approximate radial-error” of tens of meters, far exceeding the necessary sub-meter alert limit. A multi-sensor augmented-GPS/IMU system is used in the paper by R. Toledo-Moreo, et alia with “horizontal trust levels” of 7 meters to 10 meters, still an order-of-magnitude higher than the required HAV alert limit.

Multi-sensor integrity is addressed by M. Brenner, Additional Resources, but for a sensor combination specific to aviation and insufficient for terrestrial mobile robots. Other approaches to multi-sensor integration show promise, but do not provide rigorous proof of integrity. In fact, most publications use pose estimation error covariance as a measure of performance, which is understood as not being sufficient, but is the only metric currently available. Most critically, the metric does not account for fault modes introduced by feature extraction and data association, two algorithms commonly used in mobile robot localization (and discussed again below).

Unlike GPS, which gives absolute position fixes, IMUs, LiDAR, radar, and cameras provide relative displacements with respect to a previous time-step, or with respect to a map. Thus, measurement time-filtering is required, which makes integrity risk evaluation more challenging since past-time sensor errors and undetected faults can now impact current-time safety.

Example LiDAR Navigation Safety Evaluation
While safety quantification for GNSS and GNSS/INS has been rigorously performed for aviation applications, and is being researched for HAVs, navigation safety for LiDAR, radar, camera, and multi-sensor navigation is a widely unexplored research area. To provide a specific example on the research work that lies ahead, we have started developing safety risk evaluation methods for LiDARs. We selected LiDARs because of their prevalence in HAVs, of their market availability, and because of our prior experience. However, the techniques we are developing are general enough that radar, cameras, or any future sensor that returns range data can be substituted.

Raw range data must be processed before it can be used for navigation. One technique, visual odometry, establishes correlations between successive scans to estimate sensor changes in pose (i.e., position and orientation). These processes are highly computationally intensive, and have the same problems as other dead-reckoning techniques, such as wheel odometry over time. Thus, they can become inaccurate or cumbersome for HAVs moving over multiple time epochs. Although proprietary information regarding the use of visual odometry by HAV manufacturers is unavailable, the research literature suggests that it is only used for short time scale operations. A second class of algorithms provides sensor localization by extracting static features from the raw sensor data and associating those features to a map. This is typically done in two steps, as illustrated in Figure 6: feature extraction (FE) and data association (DA). The resulting information can then be iteratively processed using sequential estimators (e.g., Extended Kalman filter or EKF), which has been readily used in many practical applications.

There are several problems that the FE and DA algorithms are addressing. First, landmarks in the environment are unidentified, and their observations are not tagged in a manner similar to a GNSS satellite signal’s Pseudo Random Noise (PRN) number. Thus, the feature extraction algorithm must isolate the few most consistently identifiable, viewpoint-invariant landmarks in the raw sensor data. These features must be identifiable over repeated observations and distinguishable from one landmark to another. Features that are difficult to distinguish from each other can be found easily, but the possibility that the association is incorrect will greatly negatively impact the integrity risk.

Second, range data based on extracted features must match those features with those from a feature database or map. Data association algorithms accomplish this; however, incorrect associations commonly occur. These can lead to large navigation errors, as illustrated in Figure 6, thereby representing a threat to navigation integrity.

FE and DA can be challenging in the presence of sensor uncertainty. This is why many sophisticated algorithms have been devised. But, how can we prove whether these FE and DA methods are safe for life-critical HAV navigation applications, and under what circumstances? These research questions are currently unanswered. The most relevant publications on DA risk are found in literature on multi-target tracking. For example, in the paper Y. Bar-Shalom and T. E. Fortmann, an innovation-based nearest-neighbor DA criterion is introduced, which serves as basis in many practical implementations. The article by Y. Bar-Shalom, et alia, “The Probabilistic Data Association Filter,” provides a detailed derivation of the probability of correct association given measurements. However, this Bayesian approach is not well suited for safety-critical applications due to the lack of risk prediction capability, and to the problem of bounding the a-posteriori probability of association (a similar issue is encountered in the paper by F.C. Chan, et alia. Another insightful approach is followed in the paper by J. Areta, et alia). However, it makes approximations that do not necessarily upper-bound risks, hence do not guarantee safe operation, and it presents exact solutions that can only be evaluated using computationally expensive numerical methods, not adequate for real-time navigation. Also, the risk of FE is not addressed.

In response, we have been developing a new, computationally-efficient integrity risk prediction method to ensure safety of localization using LiDAR-based FE and DA. We have derived a multiple-hypothesis innovation-based DA method that provides the means to predict the probability of incorrect associations considering all potential landmark permutations. (For more details on these methods, see the following four papers in Additional Resources, Nos. 31, 49, 50 and 51.) We also determined a probabilistic lower bound on the minimum feature separation, which is guaranteed at FE, with pre-defined integrity risk allocation. The separation bound can be incorporated in an overall integrity risk equation. This new method was analyzed and tested to quantify the impact of incorrect associations on integrity risk. It showed that the positioning error covariance can be a misleading safety performance metric since cases were found where the contributions of incorrect associations to integrity risk far surpassed that of nominal errors accounted for in the positioning error covariance. In addition, the following key safety-tradeoff was illustrated: the more measurements are extracted, the lower the integrity risk contribution is under the correct association hypothesis, but the higher the other integrity risk contributions become because the risk of incorrect associations increases in the presence of cluttered, poorly-distinguishable landmarks. Finally, being surrounded by many landmarks increases the probability of continuous, uninterrupted navigation. The next step of this research aims at dealing with unmapped and non-static obstacles, and at quantifying the continuity risk of FE and DA.

Conclusion
Looking at the emergence of future HAV technology with the prior experience of aircraft navigation safety provides the means to scale up the challenges that lie ahead in the development of fully autonomous (Level 4 and 5) driverless cars. Many parallels can already be drawn between aviation safety requirements and early HAV standards and regulations. Still, the methods to fulfill these standards and regulations have to be established. If analytical methods are pursued, the following tasks need to be accomplished: (1) establish high-integrity raw sensor measurement error and fault models for non-GPS sensors; (2) develop analytical methods to quantify the safety risk of feature extraction and data association algorithms required in LiDAR, radar, and other pre-processing steps in camera-based localization; (3) design multi-sensor pose estimators and integrity monitors to evaluate the impact of undetected sensor faults on safety risk; and (4) derive, analyze, and experimentally implement integrity risk prediction in dynamic environments.

If these challenges are overcome, one will be able to quantify and prove the performance of an HAV’s navigation system — an essential part of safety. Proving navigation system integrity will also help give humans more confidence to trust HAVs, thus further developing the symbiotic relationship between humans and co-robots. Finally, as HAV technology progresses from driver’s aids such as active brake assist to full autonomous driving, this research is relevant now and will remain essential throughout the evolution of HAV technology.

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Enrico Salvatori, Qualcomm Europe

Carlo Bagnoli, STMicroelectronics

Multinational semiconductor and telecommunications company Qualcomm is a world leader in the design and marketing of 3G, 4G and next-generation wireless technologies. Headquartered in San Diego, California, Qualcomm has been widening its footprint in the Europe, Middle East and Africa (EMEA) region, with a core focus in Europe.

“We expect to grow Qualcomm’s presence in Europe, becoming a major EU (European Union) player in the digitization of European industries,” said Qualcomm senior vice president and president of Qualcomm Europe, Enrico Salvatori.

Multinational semiconductor and telecommunications company Qualcomm is a world leader in the design and marketing of 3G, 4G and next-generation wireless technologies. Headquartered in San Diego, California, Qualcomm has been widening its footprint in the Europe, Middle East and Africa (EMEA) region, with a core focus in Europe.

“We expect to grow Qualcomm’s presence in Europe, becoming a major EU (European Union) player in the digitization of European industries,” said Qualcomm senior vice president and president of Qualcomm Europe, Enrico Salvatori.

Part of that growth entails the company’s recently announced acquisition of Dutch-based NXP Semiconductors, a leading supplier for the secure identification, automotive and digital networking industries, a deal expected to be sealed by the end of calendar year 2017.

Key European Considerations
“Accurate, reliable, and rapid position location is an important part of the mobile experience,” Salvatori said. “We are currently continuing 5G standardization in 3GPP under an accelerated timeline, which will ultimately achieve results for all use cases for extreme mobile broad-band (MBB), massive Internet of Things (IoT) and mission-critical services, in line with the European Commission’s 5G Action Plan.”

The Commission, which is the executive arm of the EU, has been actively promoting very high-capacity networks such as 5G as Europe works to keep pace in the global wireless technologies market, citing expected worldwide 5G revenues for mobile operators in the region of €225 billion per year by 2025 (about 270 billion USD).

Salvatori said Qualcomm is proud to have established a long-lasting partnership with the European Commission (EC) and, as it happens, with the European GNSS Agency (GSA), sharing that agency’s central goal of bringing Europe’s global satellite navigation program, “Galileo,” to full fruition.

“Here at Qualcomm,” Salvatori said, “we are pursuing ongoing work on various IoT verticals, including LTE MTC/ NB-IOT and in particular C-V2X. And with LTE Release 14, we believe we can lead the way toward dedicated evolutions of 5G in addition to the NR.”

Salvatori is well-positioned to speak on such matters. He oversees Qualcomm’s European strategy for ensuring that OEMs and operators drive the latest G technology (4G/5G) adoption throughout all of Europe, in both developed and emerging markets.

Galileo in its Proper Place
For companies like Qualcomm looking to make the most of Europe’s emerging navigation and location-based services markets, the launch of live Galileo services last year was a veritable milestone.

“We were thrilled to see the European satellite system starting operations,” Salvatori said, “a real turning point for the location industry. We strongly believe that broad availability of Galileo will underpin Europe’s future innovation at home and globally, providing a backbone for the further development of the Digital Single Market and Europe’s industrial growth and competitiveness in the context of 5G, but also to the benefit of the new connected verticals.”

European authorities expect the addition of another GNSS, in the form of Galileo, will enable more accurate location performance, faster time-to-first-fix, and improved robustness all over the world, particularly in challenging urban environments where the combination of narrow streets and tall buildings can reduce accuracy.

And the launch of Galileo services was in many ways a victory for Qualcomm itself; as long-standing partners, the EC, the GSA and Qualcomm have worked in concert, with the GSA consistently highlighting Qualcomm’s role as a central player in the EU wireless technologies arena. The first European Galileo-enabled smartphone, produced by Spanish company BQ, was based on a Qualcomm chipset.

In fact, Salvatori said, “Qualcomm Technologies began implementing hardware support for Galileo several years ago in selected chipsets. And it was our company that proposed the mobile industry’s first pervasive, end-to-end, location services platform (Qualcomm® Location), for smartphone, computing, infotainment, telematics, and IoT applications.”

With optimized software enhancements, Qualcomm® Location now uses up to six satellite constellations. “Our users now benefit from more than 80 different satellites when calculating global position for navigation or location-based applications,” he said.

Salvatori said Qualcomm’s Galileo-enabled platform is being deployed broadly across the company’s modem and application processor portfolios. “This feature is integrated in the latest Qualcomm Snapdragon 800, 600, and 400 processors and modems,” he said. “And Galileo will be supported on smartphones and computing devices with the appropriate software release on Snapdragon 820, 652, 650, 625, 617, and 435 processors.”

The same goes for automotive infotainment solutions using Snapdragon 820A, and telematics and IoT solutions with Snapdragon X16, X12, X7, and X5 LTE modems, and Qualcomm 9×15 and MDM6x00 modems.

“This will also enable infotainment and telematics solution providers to satisfy an important component of the European eCall mandate ahead of the March 2018 deadline,” Salvatori said.

Low Investment, High Benefits
Salvatori said bringing Galileo into Qualcomm’s already broad location services platform presented no particular technical issues. “Supporting a new GNSS constellation does require some R&D,” he said, “but we had already implemented support for other GNSS constellations like GPS, GLONASS and BeiDou.

“For us, Galileo’s introduction in the market at commercial scale promises more innovation and business models for the expanded connectivity needs of tomorrow. This is particularly clear to us in areas such as automotive, where Galileo’s capabilities will underpin the evolution of cars toward ever greater connectivity and automation.”

Salvatori said Qualcomm’s support of Galileo is an essential extension of the company’s work in creating and evolving vehicle-to-everything communications and 5G, working closely with the mobile and automotive industries to bring these innovations to market fast and with sustained investments.

THINGS YOU SHOULD KNOW
3GPP—Third Generation Partnership Project, a collaboration between groups of telecommunications associations, known as the Organizational Partners.
LTE—Long Term Evolution, applying to the idea of improving wireless broadband speeds to meet increasing demand.
MTC—Machine Type Communication, also known as machine-to-machine communication, i.e. direct communication between devices.
NB-IOT—Narrow-Band Internet of Things, a Low-Power Wide-Area Network radio technology standard enabling a range of devices and services to be connected using cellular telecommunications bands.
C-V2X—Cellular Vehicle to Everything, combining features of V2V (Vehicle to Vehicle), V2I (Vehicle to Infrastructure), V2P (Vehicle to Pedestrian) and V2N (Vehicle to Network).
LTE Release 14—3GPP standards are structured as Releases. Discussion of 3GPP thus frequently refers to the functionality in one release or another.
NR—5G New Radio, aimed at bringing fiber-like performance to wireless broadband at a significantly lower cost per bit.
UMTS—Universal Mobile Telecommunications System, a third-generation mobile cellular system for networks based on the GSM standard.
HSPA—High Speed Packet Access commonly refers to UMTS-based 3G networks that support specialized data for improved download and upload speeds.

R&D FOREFRONT
QUALCOMM IS CURRENTLY LEADING THE “CONVEX” CONSORTIUM, which also includes Audi, Ericsson, Swarco Traffic Systems and the University of Kaiserslautern. The group’s aim is to set up a test bed for first LTE Rel. 14 trials for V2X and to validate performance and feasibility.

The project combines techniques for vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-person, (V2P), and vehicle-to-network (V2N) communications, which are considered key components for the implementation of advanced driving assistance systems and for automated driving.

The project is funded by the German Ministry of Transport and Digital Infrastructure (BMVI) in the program “Automated and Connected Driving on Digital Test Fields in Germany.”

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A new reports says that the GNSS market landscape is growing because of rapid growth of satellite technology-enabled wearables, unmanned aerial vehicles (UAVs), new innovation opportunities, and low-cost precision receivers.

ABI Research’s new GNSS integrated circuit (IC) vendor competitive analysis says that Broadcom and Qualcomm remain the two top companies for the fourth straight year.

A new reports says that the GNSS market landscape is growing because of rapid growth of satellite technology-enabled wearables, unmanned aerial vehicles (UAVs), new innovation opportunities, and low-cost precision receivers.

ABI Research’s new GNSS integrated circuit (IC) vendor competitive analysis says that Broadcom and Qualcomm remain the two top companies for the fourth straight year.

Within the past year, Broadcom grabbed more headlines with the company’s wearables success and initial work on L1/L5 dual-frequency receivers, the report said. However, the report says Qualcomm continues to lead in total GNSS shipments, as well as such innovative new technologies as LED/visible light communication and [long-term evolution] LTE Direct. Qualcomm’s partnership with Baidu on its IZat platform is also notable and represents the beginning of the era of "always on, ubiquitous location technologies," the report said.

The report lists MediaTek and u-blox in the third and fourth top market positions, respectively. "MediaTek and u-blox once again swapped places," says Patrick Connolly, ABI Research principal analyst. "U-blox had another stellar year financially and, along with Skytraq, led the way on low-cost precision GNSS with its NEO-M8P module. MediaTek, which showed significant success in wearables and smartphones, transitioned back to third place primarily due to growing market share."

The report says that CEC Huada and Samsung, both showing market innovation over the past year, are poised to instill great change in the marketplace. "CEC Huada developed single-frequency real-time kinematic (RTK) GPS, as well as [BeiDou Navigation Satellite System] receivers and INS/MEMS receivers, which the company released to select customers in 2016," Connolly said. "And it is now developing a dual-frequency BDS receiver and a receiver for [India Regional Navigation Satellite System]. Samsung, meanwhile, launched its first embedded GNSS solution, the Exynos CPU chipset. Given its presence across so many GPS-enabled consumer electronic devices, the company looks set to be a major disruptor in the coming years."

ABI said its report includes researched innovation and implementation parameters to determine the companies best positioned for success–and the companies that are in danger of losing out. In addition, the report features emerging competitive threats and technologies, ABI said.

This year, the GPS/GNSS IC market evolved into such new markets as automotive, wearables, and the internet of things (IoT), with huge potential to grow into sizeable GNSS and ubiquitous-location markets where a specific, optimized IC design will be a major plus, according to the report. "Precision GNSS techniques also came to the forefront as companies explore opportunities around vehicle-to-vehicle (V2V), advanced driver assistance (ADAS), and driverless cars. This is now the most significant design trend in the industry, with consumer GNSS IC vendors facing stiff competition for precision GNSS incumbents like Trimble and Novatel, as well as a number of new interesting start-ups," the report said. "Finally, new entrants like Intel and CEC Huada continue to expand their offering, while Samsung’s entry could significantly change the market share dynamic."

Companies featured in the study include Broadcom, CEC Huada, Galileo Satellite Navigation, Intel, MediaTek, Qualcomm, Samsung, SkyTraq Technology, STMicroelectronics, and u-blox.

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The navigation sensors for location-based services (LBS) are complex technical systems. Modern technical science can answer most questions about the optimality of particular position determination methods, signal processing algorithms, electronic circuits or similar well-defined problems, but the rigorous answer to the questions concerning the optimal LBS positioning sensor are still a big problem.

The navigation sensors for location-based services (LBS) are complex technical systems. Modern technical science can answer most questions about the optimality of particular position determination methods, signal processing algorithms, electronic circuits or similar well-defined problems, but the rigorous answer to the questions concerning the optimal LBS positioning sensor are still a big problem.

Ivan Petrovski’s book presents not only LBS navigation sensors based mainly on satellite navigation systems (GNSS) and software radio, but also it provides valuable insight from their practical implementation. GPS, GLONASS, Galileo, and BeiDou for Mobile Devices, From Instant to Precise Positioning is written for satellite navigation experts. It summarizes the latest knowledge of mobile GNSS devices’ design and applications as well as the opinion of the author on the realization of the particular problems in the area of the GNSS signal processing and software radio, all based on his deep experience.

In contrast to Dr. Petrovski’s previous book, Digital Satellite Navigation and Geophysics, or Bernard Hoffmann-Wellenhof’s GNSS-Global Navigation Satellite Systems — GPS, GLONASS, Galileo & More, the valuable information is not placed in a wider context; so, beginners could easily lose their orientation.

The book is introduced by an inspired foreword to location-based services by Glen Gibbons. The 312 pages of the book are then organized into four parts as follows:

The first part describes advanced knowledge of navigation satellite position calculations, satellite navigation signals, and stand-alone and reference station–based GNSS positioning. The most valuable parts are modeling of the signal propagation effects and carrier phase positioning.

The second part deals with the conventional and software GNSS receivers. There is a lot of practically usable information such as advice on how to design high- or super-high–sensitivity receivers, how to optimally implement GNSS correlators to the general purpose processor and many others.

The third part addresses the essential LBS relative positioning methods including assisted-GNSS, real-time kinematic, differential GNSS, and pseudolites as well as instant BGPS (developed by Dr. Petrovski and others), precise point positioning, and snapshot methods. The presented text is not limited to describing those methods, but the author also gives practical examples of applications and provides recommendations for their implementation. Very well written is the last chapter, “Trends, Opportunities and Prospects,” in which Ivan Petrovski predicts convergence of the mobile and geodetic applications and tight integration with the Internet.

The last part deals with the GNSS receiver and LBS sensor testing. I personally use the IP-Solutions ReGen GPS simulator developed by the author for testing of LEO satellite GPS receivers. I fully agree with his attitude that the software GNSS simulators have good prospects and, thanks to the increased performance of general purpose processors, they will become a full-blown alternative to standard software-designed radio (SDR) simulators.

This book is definitely a valuable source of information for development, optimization, and testing of GNSS signals and data processing algorithms and their implementation to the software radio for mobile, indoor, and high-grade receivers.

GPS, GLONASS, Galileo, and BeiDou for Mobile Devices, From Instant to Precise Positioning
By Ivan G. Petrovski, Cambridge University Press, 2014, ISBN 978-1-107-03584-3

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The 10th International ESA Conference on Guidance, Navigation & Control Systems will take place on May 29 – June 2 2017, at the Crowne Plaza Salzburg – The Pitter in Salzburg, Austria.

Registration opens on February 15, 2017, early registration ends March 15. 

The 10th International ESA Conference on Guidance, Navigation & Control Systems will take place on May 29 – June 2 2017, at the Crowne Plaza Salzburg – The Pitter in Salzburg, Austria.

Registration opens on February 15, 2017, early registration ends March 15. 

The GNC 2017 Conference & Exhibition provides an opportunity to promote GNC products & activities, meet potential customers, exchange ideas and encourage future cooperation. The Conference is addressed to international participants from the Aerospace Industry, Academia, Equipment Manufacturers and Space Agencies.

The Product and Poster Exhibition will have an extended dedicated session on May 30, 2017. The aim of the exhibition is to provide a platform for manufacturers of products used in the GNC subsystems to publicize and promote their products and for the potential users to obtain a full and up to date overview of the currently available and next generation products.

Sessions include:

• Advances in Sensors and Actuators
• Advances in Control
• Current missions
• Future Missions
• In-Orbit Experiences and Demonstrators
• Students Session
• GNC for Space Transportation
• GNC for Exploration Missions
• Debris mitigation and removal
• High accuracy pointing
• Autonomy, FDIR and Ground Control
• Autonomous navigation
• Small satellites & miniaturization

The scope of the exhibition includes:

• Sensors for AOCS/GNC
• Actuators for AOCS/GNC
• AOCS/GNC Simulation sw tools
• AOCS/GNC Design sw tools
• AOCS OGSE and EGSE
• AOCS/GNC Detector Technology demonstrators

The event is organized by the European Space Agency (ESA).

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AUVSI’s XPONENTIAL 2017, All Things Unmanned will take place at the Kay Bailey Hutchison Convention Center in Dallas, Texas, U.S.A. from May 8 – 11, 2017.

Online registration is open. Early bird registration ends November 30, 2016. Onsite registration will be available.

AUVSI’s XPONENTIAL 2017, All Things Unmanned will take place at the Kay Bailey Hutchison Convention Center in Dallas, Texas, U.S.A. from May 8 – 11, 2017.

Online registration is open. Early bird registration ends November 30, 2016. Onsite registration will be available.

The conference features technical panels and presentations, workshops and poster sessions on the state of the unmanned systems market. It covers military, civil and commercial applications for air, ground and maritime vehicles.

This year, the conference will start on Monday, May 8 with educational sessions. The three educational tracks are Policy, Technology and Business Solutions. The commercial exhibition will start on May 9, including the Technology Pavilions and XPONENTIAL Educational Programs: Robots in Action, Solutions Theatre, Poster Presentations, The Starting Point and RoboNation.

Over 200 sessions will cover the following markets:

• Aerospace
• Agriculture
• Automated Vehicles
• Business Solutions
• Cinematography
• Construction
• Energy
• Government Defense
• Ground
• Mapping & Surveying
• Maritime
• Oil & Gas
• Policy
• Public Safety
• Railroad
• Technology
• Wireless

Keynote speakers include Brian Krzanich, CEO, Intel Corporation, and Jim Cantore, The Weather Channel (Weather) storm tracker.

AUVSI, the Association for Unmanned Vehicle Systems International, is a nonprofit industry organization of over 7,500 members headquartered in Washington D.C.

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The 2016 Trimble Dimensions user conference and exhibition will take place at the Venetian Hotel in Las Vegas on November 7, 8 and 9.

The annual event gathers users of Trimble’s products including positioning technology for unmanned systems as well as mapping, GIS, surveying, photgrammetry and remote sensing and other technologies of interest to readers of Inside GNSS.

Four hundred and fifty technical sessions and networking events give attendees an opportunity to network widely within and among industry groups.

The 2016 Trimble Dimensions user conference and exhibition will take place at the Venetian Hotel in Las Vegas on November 7, 8 and 9.

The annual event gathers users of Trimble’s products including positioning technology for unmanned systems as well as mapping, GIS, surveying, photgrammetry and remote sensing and other technologies of interest to readers of Inside GNSS.

Four hundred and fifty technical sessions and networking events give attendees an opportunity to network widely within and among industry groups.

If you are an expert in a field covered by the conference and are an experienced presenter in front of large audiences, Trimble will welcome your proposal for speaking at the event.The organizers are accepting abstracts until 20 here.

Early bird pricing ends on July 31.

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Chris Bartone, Ohio University

GNSS receivers seem to get all the attention. Go to any technical GNSS conference and the lion’s share of presentations are about receiver design and techniques: better algorithms, signal processing, integration with other sensors, spoofing detection, and on and on.

GNSS receivers seem to get all the attention. Go to any technical GNSS conference and the lion’s share of presentations are about receiver design and techniques: better algorithms, signal processing, integration with other sensors, spoofing detection, and on and on.

But here’s a fundamental fact of radio science: without antennas, GNSS receivers are essentially useless. Antennas are the component that picks up the GNSS signals out of the RF noise and channels them to the receiver proper — the better the antenna, the better the signals that receivers have to process.

Moreover, largely constrained by the laws of physics, the physical aspects of antennas play a substantial role in the size, weight, and power parameters within which receiver designers must work. And, by extension, antennas are a key variable in the cost factors associated with receiver manufacturing.

With these factors in mind, we turned to Dr. Inder “Jiti” Gupta for insights into the current state of GNSS antennas and their role in GNSS positioning, navigation, and timing. Currently a research professor with the Department of Electrical and Computer Engineering of The Ohio State University, Gupta has focused on GNSS antennas and antenna electronics for the past 17 years. An Edmond S. Gillespie Fellow of the Antenna Measurement Techniques Association (AMTA) the recipient of the 2007 AMTA Distinguished Achievement Award, he has authored more than 150 journal and conference papers.

IGM: What changes are taking place in the GNSS operational environment that pose increasing challenges for successful PNT applications?

GUPTA: Many changes are taking place, including increased GNSS operation in dense urban environments, inside buildings, on platforms that change rapidly with time, e.g., rotorcrafts. The major challenge, however, is posed by spectrum crowding and radio frequency interference (RFI) that could be intentional or unintentional. Spectrum crowding will lead to high-energy signals next to GNSS frequency bands and will require filters with very narrow passband and very high rejection ratio outside the pass band.

One will be looking at brick wall type of filters that are not only costly but can distort the signals of interest (satellite signals). RFI is within the GNSS signals frequency band and cannot be filtered in the frequency domain without affecting the satellite signals. Other approaches need to be applied for successful operation of GNSS receivers under strong RFI environments.

IGM: Can improved receiver antenna design help in these operational environments?

GUPTA: Yes. Currently, fixed-reception-pattern antennas (usually a single element and single feed) are used with GNSS receivers. As the name indicates, the response of these antennas does not change with the RF environment. If we replace these antennas with multiple element antennas whose weights can be controlled (adapted) in real time, then we can easily obtain spatial and polarization discrimination. For example, the signals received by various antenna elements can be combined to increase the gain along selected GNSS satellites.

One can also adapt the element weights to steer antenna nulls along the sources of RFI. Note that controlled reception pattern antennas (CRPA) used with many military GNSS receivers carry out null steering. One can combine beamforming with null steering to increase the antenna gain along the satellite direction while suppressing the RFI simultaneously. One can form ring nulls to suppress multipath or RFI originating around the horizon.

For applications where it is not possible to install antennas with multiple elements, one can use multiple feeds with a single aperture (microstrip patch antennas) and use the output of these feeds to carry out null steering and/or polarization discrimination.

IGM: What innovations in receiver/antenna software seem most promising?

GUPTA: I do not know if we can call it innovation or not, but array signal processing is one area that has not been exploited by GNSS receiver designers. Only recently has the navigation community started using this powerful technology to enhance the receiver performance in strong multipath and RFI environments and to geolocate the sources of interfering signals. With the advancements in field programmable gate arrays (FPGAs), we need to incorporate array signal processing in GNSS receivers.

IGM: Some application developers and handset manufacturer have expressed interest in implementing multi-GNSS capability in consumer products. What should be the considerations for antenna design & development to support the implementation of such capability?

GUPTA: A GNSS antenna is supposed to have omnidirectional (for handheld receivers) or upper hemispherical coverage (for mounted receivers). Thus, these antennas should be low directivity antennas. Also, a GNSS antenna should be an efficient antenna.

Two main factors dictate the antenna efficiency. First, how well is the antenna matched to the receiver? This is also called the return loss of the antenna (S₁₁ parameter). A good number to shoot for is better than 10 decibels over all the frequency bands. The second factor is the radiation efficiency of the antenna which tells us how much of the incident RF energy antenna passes to the receiver. A good number to shoot for is better than 75 percent radiation efficiency over all frequency bands.

Another parameter to consider during the design and development is the antenna polarization. GNSS signals have right hand circular (RHC) polarization. For the best performance, GNSS antennas should have RHC polarization over all the frequency bands and field of view, which is upper hemisphere for mounted receivers and whole sphere for handheld receivers.

IGM: What challenges does one face in designing antennas for handheld multi-GNSS receivers?

GUPTA: For handheld GNSS receivers, major challenges are size and weight. The current commercial handheld GNSS receivers use GPS L1 C/A coded signals or maybe an L1 band GLONASS signal. The bandwidth of these signals is approximately two megahertz at 1575.42 MHz. The percentage bandwidth, thus, is very small.

It is easy to design a lightweight antenna with small volume for small percentage bandwidth. As the bandwidth increases, it becomes more and more difficult to make the antenna small without losing its efficiency. Either one has to use multiple antennas to cover all the frequency bands or use frequency independent antennas (spiral type antennas). In both cases, one will need more real estate, and that is a challenge for handheld GNSS receivers. In this case, one may want to consider wearable antennas for multi-GNSS receivers.

IGM: What are the relative strengths and weaknesses of anechoic chamber measurements versus real-world trials for testing of GNSS antennas?

GUPTA: Let us start with the weakness. The major weakness of anechoic chamber measurements is that it is difficult to simulate the real-world physical environment. Let us say that we are interested in measuring a GNSS receiver antenna mounted on a large SUV. I do not know many anechoic chambers that are large enough to measure antennas mounted on large SUV at GNSS frequency bands. Also, it is hard to duplicate the surroundings.

On the other hand, anechoic chamber provides a very controlled RF environment. One can choose what signals to be simulated and the relative strengths of those signals. For example, one can transmit very strong (more than 20-decibel signal-to-noise ratio) signals in GNSS frequency bands to measure the antenna response (gain and phase) at those frequencies. Thus, one can obtain very accurate antenna response without very long integration. One can also sweep the frequency to cover the whole frequency band of interest. With current technology, frequency sweep is trivial and extremely fast. One can cover the whole L-band in a few seconds. Also, using two independent motion controls, one can control the attitude of the antenna under test to measure its response over the whole field of view. Thus, anechoic chambers are well suited to verify the antenna design by measurements.

The post GNSS Antennas with Dr. Inder Gupta appeared first on Inside GNSS - Global Navigation Satellite Systems Engineering, Policy, and Design.

AUVSI’s Unmanned Systems conference and trade show is now XPONENTIAL.

XPONENTIAL 2016 will take place at the Ernest N. Morial Convention Center in New Orleans, Louisiana, U.S.A. from May 2 – 5, 2016.

AUVSI’s Unmanned Systems conference and trade show is now XPONENTIAL.

XPONENTIAL 2016 will take place at the Ernest N. Morial Convention Center in New Orleans, Louisiana, U.S.A. from May 2 – 5, 2016.

Online registration is open. Onsite registration will be available.

The conference features technical panels and presentations, workshops and poster sessions on the state of the unmanned systems market. It covers military, civil and commercial applications for air, ground and maritime vehicles.

This year, the conference will start on Monday, May 2 with technical, panel and interactive workshop sessions. All participants will have access to the daily general sessions. The commercial exhibition will start on May 3, including the Innovation Hub with poster sessions, special interest talks, knowledge bar and Beyond the Booth presentations.

The conference program is under development.

AUVSI, the Association for Unmanned Vehicle Systems International, is a nonprofit industry organization of over 7,500 members headquartered in Washington D.C.

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