SaPPART White paper. Better use of Global Navigation Satellite Systems for safer and greener transport

Transport and mobility services are crucial to the society that faces important challenges. Up to date, transport facilities and services have been fundamental to economic growth. However, there have significant and unacceptable negative impacts on the environment including pollution, noise and climate change. Therefore, it is paramount that the efficiency of the transport system is improved significantly including lower consumption of energy. A way of achieving this is through the concept of smart transport that exploits Intelligent Transport Systems (ITS) technology. ITS are built on three technology pillars: information, communication and positioning technologies. Of the three technologies, positioning could be argued to be the least familiar amongst transport stakeholders. However, a quick investigation reveals that there are a wide variety of transport and related services often associated with communication technologies that are supported by positioning. Currently, the positioning is provided in the majority of the cases by Global Navigation Satellite System (GNSS), among which the Global Positioning System (GPS) is the pioneer and still the most widely used system. The other current fully operational stand-alone system is Russia’s GLONASS. As these operational systems were not originally and specifically designed for transport applications, the actual capabilities and limitations of the current GNSS are not fully understood by many stakeholders. Therefore, better knowledge of these limitations and their resolution should enable a much more rapid deployment of ITS. This white paper is produced by the members of the COST Action SaPPART with two principal aims. The first is to explain the principles, state-of-the-art performance of GNSS technology and added value in the field of transport. The second aim is to deliver key messages to the stakeholders to facilitate the deployment of GNSS technology and thus contribute to the development of smarter and greener transport systems. The first chapter highlights the important role of positioning in today transport systems and the added value of accurate and reliable positioning for critical systems. The second chapter is about positioning technologies for transport: GNSS and their different aiding and augmentation methods are described, but the other complementary technologies are also introduced. The third and last chapter is about the management of performances inside a positioning-based intelligent transport system, between the positioning system itself and the application-specific part of the system which processes the raw position for delivering its service

A stake-out prototype system based on GNSS-RTK technology for implementing accurate vehicle reliability and performance tests

none4noThere are many car tests regulated by European and international standards and carried out on tracks to assess vehicle performance. The test preparation phase usually consists of placing road cones on the track with a specific configuration defined by the considered standard; this phase is performed by human operators using imprecise and slow methods, mainly due to the huge required distances. In this paper, a new geolocation stake-out system based on GNSS RTK technology has been realized and tested, supported by a Matlab-based software application to allow the user to quickly and precisely locate the on-track points on which to position the road cones. The realized stake-out system, innovative and very simple to use, produces negligible average errors (i.e. 2.4-2.9 cm) on the distance between the staked-out points according to the reference standards (distance percentage error 0.29-0.47%). Furthermore, the measured average angular error is also very low, in the range 0.04-0.18°. Finally, ISO 3888-1 and ISO 3888-2 test configurations were re-produced on the proving ground of the Porsche Technical Center by utilizing the realized stake-out system to perform a double lane-change manoeuvre on car prototypes.Special Issue "Electronic Systems and Energy Harvesting Methods for Automation, Mechatronics and Automotive 2021" Article Number: 4885openP. Visconti; F. Iaia, R. de Fazio, I. GiannoccaroVisconti, P.; Iaia, F.; de Fazio, R.; Giannoccaro, I

DGNSS Cooperative Positioning in Mobile Smart Devices: A Proof of Concept

Global Navigation Satellite System (GNSS) constitutes the foremost provider for geo-localization in a growing number of consumer-grade applications and services supporting urban mobility. Therefore, low-cost and ultra-low-cost, embedded GNSS receivers have become ubiquitous in mobile devices such as smartphones and consumer electronics to a large extent. However, limited sky visibility and multipath scattering induced in urban areas hinder positioning and navigation capabilities, thus threatening the quality of position estimates. This work leverages the availability of raw GNSS measurements in ultralow-cost smartphone chipsets and the ubiquitous connectivity provided by modern, low-latency network infrastructures to enable a Cooperative Positioning (CP) framework. A Proof Of Concept is presented that aims at demonstrating the feasibility of a GNSS-only CP among networked smartphones embedding ultra-low-cost GNSS receivers. The test campaign presented in this study assessed the feasibility of a client-server approach over 4G/LTE network connectivity. Results demonstrated an overall service availability above 80%, and an average accuracy improvement over the 40% w.r.t. to the GNSS standalone solution

Advanced Integration of GNSS and External Sensors for Autonomous Mobility Applications

L'abstract è presente nell'allegato / the abstract is in the attachmen

Methods for Improving Performance in Consumer Grade GNSS Receivers

Viimeisten kolmen vuosikymmenen aikana satelliittinavigointi on kehittynyt ammatti ja sotilaskäyttäjien tekniikasta kaikkien saatavilla olevaksi tekniikaksi. Varsinkin viimeisen 15 vuoden aikana, kun vastaanottimet alkoivat pienentyä ja halpenivat, on lisääntynyt määrä yrityksiä, jotka toimittavat GPS-laitteita satoihin erilaisiin sovelluksiin. Kaikille moderneille tekniikoille on myös tyypillistä, että tutkimukseen ja siihen liittyvään vastaanottimien kehittämiseen on käytetty valtavasti rahaa, mikä on johtanut huomattavaan parantumiseen vastaanottimen suorituskyvyssä. GPS-vastaanottimien kehitystyön lisäksi uusien maailmanlaajuisten satelliittinavigointijärjestelmien, kuten venäläisen GLONASS, kiinalaisen BeiDou- ja eurooppalaisen Galileo-järjestelmien käyttöönotto tarjoaa entistä enemmän mahdollisuuksia suorituskyvyn parantamiseen. Sekä GPS että nämä uudet järjestelmät ovat myös ottaneet käyttöön uudentyyppisiä signaalirakenteita, jotka voivat tarjota parempilaatuisia havaintoja ja siten parantaa kaikkien vastaanottimien suorituskykyä. Lopuksi menetelmät, kuten PPP ja RTK, jotka aiemmin olivat varattu ammattikäyttäjille, ovat tulleet kuluttajamarkkinoille mahdollistaen ennennäkemättömän suorituskyvyn jokaiselle satelliittinavigointivastaanottimien käyttäjälle. Tässä opinnäytetyössä arvioidaan tämän kehityksen vaikutusta sekä suorituskykyyn että vastaanottimen arkkitehtuuriin. Työssä esitellään yksityiskohtaisesti FGI:ssä kehitetyn ohjelmistopohjaisen vastaanottimen, FGI-GSRx:n. Tämän vastaanottimen avulla on työssä arvioitu miten sekä uudet konstellaatiot että uudet nykyaikaiset signaalit ja niitten seurantamenetelmät vaikuttavat suorituskykyyn ja vastaanotin arkkitehtuuriin. Tämän lisäksi on arvioitu PPP- ja RTK-tarkkuuspaikannusmenetelmien vaikutus FinnRefCORS-verkkoa käyttäen useiden erityyppisten vastaanottimien kanssa, mukaan lukien kuluttajalaatuiset vastaanottimet. Tulokset osoittavat, että enemmän konstellaatioita ja signaaleja käytettäessä paikannusratkaisun tarkkuus paranee 3 metristä 1,4 metriin hyvissä olosuhteissa ja yli 10-kertaiseksi tiheästi rakennetuissa kaupungeissa, jossa käytettävissä olevien signaalien määrä kasvaa kertoimella 2 käytettäessä kolmea konstellaatiota. Uusia moderneja modulaatiotekniikoita, kuten BOC-modulaatiota, käytettäessä tulokset osoittavat Galileo-ratkaisun tarkkuuden paranevan lähes 25%:lla ja esitelty uusi signaalinkäsittelymenetelmä lisää tällaisen tarkkuuden saatavuutta 50%:sta lähes 100%:iin. Lopuksi tarkkuuspaikannusmenetelmien tulokset osoittavat, että 15 cm:n tarkkuus on saavutettavissa, mikä on merkittävä parannus verrattuna 1,4 metrin tarkkuuteen. Näiden parannusten saavuttamiseksi on olennaista, että itse vastaanotin on mukautettu hyödyntämään näitä uusia signaaleja ja konstellaatioita. Tämä tarkoittaa, että nykyaikaisten kuluttajamarkkinoiden vastaanottimien suunnittelu on haastavaa ja monissa tapauksissa ohjelmistopohjainen vastaanotin olisi parempi ja halvempi valinta kuin uusien mikropiirien kehittäminen.For the last three decades, satellite navigation has evolved from being a technology for professional and military users to a technology available for everyone. Especially during the last 15 years, since the receivers started getting smaller and cheaper, there has been an increasing number of companies delivering Global Positioning System (GPS) enabled devices for hundreds of different kind of applications. Typical for any modern technology, there has also been an enormous amount of money spent on research and accompanied receiver development resulting in an immense increase in receiver performance. In addition to the development efforts on GPS receivers the introduction of new global navigation satellite systems such as the Russian Globalnaja Navigatsionnaja Sputnikovaja Sistema (GLONASS), the Chinese BeiDou, and the European Galileo systems offers even more opportunities for improved performance. Both GPS and these new systems have also introduced new types of signal structures that can provide better quality observations and even further improve the performance of all receivers. Finally, methods like Precise Point Positioning (PPP) and Real Time Kinematic (RTK) that earlier were reserved for professional users have entered into the consumer market enabling never before seen performance for every user of satellite navigation receivers. This thesis will assess the impact of this development on both performance as well as on receiver architecture. The design of the software defined receiver developed at FGI, the FGI-GSRx, is presented in detail in this thesis. This receiver has then been used to assess the impact of using multiple constellations as well as new novel signal processing methods for modern signals. To evaluate the impact of PPP and RTK methods the FinnRef Continuously Operating Reference Station (CORS) network has been used together with several different types of receivers including consumer grade off the shelf receivers. The results show that when using more constellations and signals the accuracy of the positioning solution improves from3 meters to 1.4 meters in open sky conditions and by more than a factor 10 in severe urban canyons. For severe urban canyons the available also increases by a factor 2 when using three constellations. When using new modern modulation techniques like high order BOC results show an accuracy improvement for a Galileo solution of almost 25 % and the presented new signal processing method increase the availability of such an accuracy from 50 % to almost 100 %. Finally, results from precise point positioning methods show that an accuracy of 15 cm is achievable, which is a significant improvement compared to an accuracy of 1.4 m for a standalone multi constellation solution. To achieve these improvements, it is essential that the receiver itself is adapted to make use of these new signals and constellations. This means that the design of modern consumer market receivers is challenging and in many cases a software define receiver would be a better and cheaper choice than developing new Application Specific Integrated Circuit (ASIC)’s

Roadmap on signal processing for next generation measurement systems

Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.AerodynamicsMicrowave Sensing, Signals & System

Floating Car Data Technology

The limiting conditions of traffic in cities, together with the complex and dynamic traffic flows, require an efficient and systematic management and information provision for the traffic participants, with the goal to achieve better utilisation of traffic resources and preserve sustainable mobility. In that context, it is important to identify the traffic flow location features, which requires data and information. This paper presents the application of mobile vehicles for the collection of real time traffic flow data. Such data have become an important source of traffic data, since they can be collected in a simple and cost-efficient way, enabling higher coverage than the conventional approaches, despite the reliability issues. The term referring to that type of data collection, commonly used in scientific and professional literature is FCD (Floating Car Data) and “Probe vehicle”. The efficiency presentation of applying this extensive data source for retrieving necessary parameters and information related to the achievement of sustainable mobility is the final objective of this paper. A description of modern technologies that serve as a basis for probe vehicle data collection has been provided: a geographical information system (GIS), global navigation satellite system (GNSS) and related wireless communication. Within the key technologies review, the development possibilities of data collection by mobile sensors have also been presented

Cooperative Relative Positioning for Vehicular Environments

Fahrerassistenzsysteme sind ein wesentlicher Baustein zur Steigerung der Sicherheit im Straßenverkehr. Vor allem sicherheitsrelevante Applikationen benötigen eine genaue Information über den Ort und der Geschwindigkeit der Fahrzeuge in der unmittelbaren Umgebung, um mögliche Gefahrensituationen vorherzusehen, den Fahrer zu warnen oder eigenständig einzugreifen. Repräsentative Beispiele für Assistenzsysteme, die auf eine genaue, kontinuierliche und zuverlässige Relativpositionierung anderer Verkehrsteilnehmer angewiesen sind, sind Notbremsassitenten, Spurwechselassitenten und Abstandsregeltempomate. Moderne Lösungsansätze benutzen Umfeldsensorik wie zum Beispiel Radar, Laser Scanner oder Kameras, um die Position benachbarter Fahrzeuge zu schätzen. Dieser Sensorsysteme gemeinsame Nachteile sind deren limitierte Erfassungsreichweite und die Notwendigkeit einer direkten und nicht blockierten Sichtlinie zum Nachbarfahrzeug. Kooperative Lösungen basierend auf einer Fahrzeug-zu-Fahrzeug Kommunikation können die eigene Wahrnehmungsreichweite erhöhen, in dem Positionsinformationen zwischen den Verkehrsteilnehmern ausgetauscht werden. In dieser Dissertation soll die Möglichkeit der kooperativen Relativpositionierung von Straßenfahrzeugen mittels Fahrzeug-zu-Fahrzeug Kommunikation auf ihre Genauigkeit, Kontinuität und Robustheit untersucht werden. Anstatt die in jedem Fahrzeug unabhängig ermittelte Position zu übertragen, werden in einem neuartigem Ansatz GNSS-Rohdaten, wie Pseudoranges und Doppler-Messungen, ausgetauscht. Dies hat den Vorteil, dass sich korrelierte Fehler in beiden Fahrzeugen potentiell herauskürzen. Dies wird in dieser Dissertation mathematisch untersucht, simulativ modelliert und experimentell verifiziert. Um die Zuverlässigkeit und Kontinuität auch in "gestörten" Umgebungen zu erhöhen, werden in einem Bayesischen Filter die GNSS-Rohdaten mit Inertialsensormessungen aus zwei Fahrzeugen fusioniert. Die Validierung des Sensorfusionsansatzes wurde im Rahmen dieser Dissertation in einem Verkehrs- sowie in einem GNSS-Simulator durchgeführt. Zur experimentellen Untersuchung wurden zwei Testfahrzeuge mit den verschiedenen Sensoren ausgestattet und Messungen in diversen Umgebungen gefahren. In dieser Arbeit wird gezeigt, dass auf Autobahnen, die Relativposition eines anderen Fahrzeugs mit einer Genauigkeit von unter einem Meter kontinuierlich geschätzt werden kann. Eine hohe Zuverlässigkeit in der longitudinalen und lateralen Richtung können erzielt werden und das System erweist 90% der Zeit eine Unsicherheit unter 2.5m. In ländlichen Umgebungen wächst die Unsicherheit in der relativen Position. Mit Hilfe der on-board Sensoren können Fehler bei der Fahrt durch Wälder und Dörfer korrekt gestützt werden. In städtischen Umgebungen werden die Limitierungen des Systems deutlich. Durch die erschwerte Schätzung der Fahrtrichtung des Ego-Fahrzeugs ist vor Allem die longitudinale Komponente der Relativen Position in städtischen Umgebungen stark verfälscht.Advanced driver assistance systems play an important role in increasing the safety on today's roads. The knowledge about the other vehicles' positions is a fundamental prerequisite for numerous safety critical applications, making it possible to foresee critical situations, warn the driver or autonomously intervene. Forward collision avoidance systems, lane change assistants or adaptive cruise control are examples of safety relevant applications that require an accurate, continuous and reliable relative position of surrounding vehicles. Currently, the positions of surrounding vehicles is estimated by measuring the distance with e.g. radar, laser scanners or camera systems. However, all these techniques have limitations in their perception range, as all of them can only detect objects in their line-of-sight. The limited perception range of today's vehicles can be extended in future by using cooperative approaches based on Vehicle-to-Vehicle (V2V) communication. In this thesis, the capabilities of cooperative relative positioning for vehicles will be assessed in terms of its accuracy, continuity and reliability. A novel approach where Global Navigation Satellite System (GNSS) raw data is exchanged between the vehicles is presented. Vehicles use GNSS pseudorange and Doppler measurements from surrounding vehicles to estimate the relative positioning vector in a cooperative way. In this thesis, this approach is shown to outperform the absolute position subtraction as it is able to effectively cancel out common errors to both GNSS receivers. This is modeled theoretically and demonstrated empirically using simulated signals from a GNSS constellation simulator. In order to cope with GNSS outages and to have a sufficiently good relative position estimate even in strong multipath environments, a sensor fusion approach is proposed. In addition to the GNSS raw data, inertial measurements from speedometers, accelerometers and turn rate sensors from each vehicle are exchanged over V2V communication links. A Bayesian approach is applied to consider the uncertainties inherently to each of the information sources. In a dynamic Bayesian network, the temporal relationship of the relative position estimate is predicted by using relative vehicle movement models. Also real world measurements in highway, rural and urban scenarios are performed in the scope of this work to demonstrate the performance of the cooperative relative positioning approach based on sensor fusion. The results show that the relative position of another vehicle towards the ego vehicle can be estimated with sub-meter accuracy in highway scenarios. Here, good reliability and 90% availability with an uncertainty of less than 2.5m is achieved. In rural environments, drives through forests and towns are correctly bridged with the support of on-board sensors. In an urban environment, the difficult estimation of the ego vehicle heading has a mayor impact in the relative position estimate, yielding large errors in its longitudinal component

Advanced Location-Based Technologies and Services

Since the publication of the first edition in 2004, advances in mobile devices, positioning sensors, WiFi fingerprinting, and wireless communications, among others, have paved the way for developing new and advanced location-based services (LBSs). This second edition provides up-to-date information on LBSs, including WiFi fingerprinting, mobile computing, geospatial clouds, geospatial data mining, location privacy, and location-based social networking. It also includes new chapters on application areas such as LBSs for public health, indoor navigation, and advertising. In addition, the chapter on remote sensing has been revised to address advancements