Centimeter-Level GNSS Positioning Using C-ITS for Correction Data Delivery: An Experimental Study

In this demo, we introduce a Human-Interactive Hardware-In-the-Loop Simulation (HI2LS) framework for evaluating Cooperative Intelligent Transportation Systems (C-ITS) and road safety services. C-ITS is a complex system consisting of Wireless Access in Vehicular Environments (WAVE) communication devices, Human-Machine Interface (HMI), and computing elements to evaluate collision risk. Furthermore, C-ITS and services require the high reliability in a complicated operating environments including the human factors, and thus system testing is very important and essential process. In this demo, we propose a HI2LS framework for designing and evaluating road safety systems based on vehicle-to-vehicle/-infrastructure (V2X) communications. It enables developers to evaluate their road safety system designs in a wide range of virtual traffic environments and road conditions, which can replace dangerous and time-consuming field tests while guaranteeing the high reliability.

Cooperative Intelligent Transport Systems (C-ITS) have seen increased interest in recent years, with several ongoing activities and first practical implementations. While clearly V2X-communication will play a significant role in all C-ITS deployments, several key components, such as the wireless communication technology and the message formats, are still evolving. This calls for a modular approach when designing C-ITS infrastructure, especially when considering a real world urban environment. Such environments are characterized by heterogeneous traffic light hardware and traffic management systems.This paper focuses on the modular architecture of the C-ITS pilot in Dresden. For example, a highly flexible communication stack is developed for this special purpose. It fully supports the message types standardized by European Telecommunications Standards Institute (ETSI) on the one hand, while on the other hand allowing the rapid implementation and test of novel message types and contents. This went hand in hand with the development of a modular roadside unit, enabling the rapid implementation of service applications on its central processing unit. In combination with a cloud-based backend, a real-time C-ITS service platform with a hybrid communication concept is developed.This paper presents a modular architecture and best practice guidelines based on experiences of the C-ITS pilot in Dresden. First results of research topics, such as latency measurements, are shown.The ongoing C-ITS deployment in Dresden is part of the initiative Synchrone Mobilitat 2023, which aims at advancing Intelligent Transport Systems. Focusing on automated and connected driving in urban areas, the research and development projects under its umbrella initiate scientific and technological developments and provide comprehensive test facilities. Automated and connected driving in urban areas requires not only simulations and test drives on test sites but also enormous testing effort under real traffic conditions. The Dresden Testbed offers outstanding conditions to conduct real-world test with a multitude of test kilometers and the experience of different traffic scenarios.

Accurate and reliable vehicle positioning is a prerequisite for the development of cooperative intelligent transportation systems (C-ITSs), especially for automated driving applications and in scenarios where global navigation satellite systems (GNSSs) fail to meet the requirements of safety-critical services. This article presents an experimental assessment of a roadside infrastructure-based localization system that exploits a wideband wireless technology to estimate a vehicle’s position and communicates this information in real time to the vehicle via a vehicle-to-everything (V2X) link. We highlight the need to use advanced tracking methods to cope with heterogeneous vehicle dynamics. We validate the proposed system experimentally in both racetrack and highway tunnel scenarios using an ultra wideband (UWB) positioning network, showcasing the system’s feasibility through on-field implementations. The findings reveal that the wideband radio technology obtains positioning accuracy comparable to GNSS real-time kinematic with a mismatch lower than 30 cm in 95% of the cases. This capability provides a seamless high-accuracy positioning service and successfully unlocks autonomous navigation in tunnels. This article underscores the key role of the roadside infrastructure in enhancing autonomous vehicle navigation by integrating a precise positioning technology as UWB with V2X communication, thereby meeting the stringent requirements of safety-critical C-ITS applications.

Cooperative Intelligent Transport Systems (C-ITS) aim to enhance road safety, traffic efficiency and environmental performance of road transportation by enabling communication between the vehicles and the road infrastructure. Today, vehicle manufacturers and road operators are investing significantly in C-ITS technology and infrastructure implementation to realise its potential benefits. In Europe, the C-Roads platform aims to harmonise the deployment of C-ITS infrastructure and services across Europe. As part of the C-Roads platform, Transport Infrastructure Ireland implemented C-ITS pilot infrastructure to deliver C-ITS services to road users via ITS-G5 and cellular communications. For the deployment of ITS-G5 Roadside Units along a 60 km distance of the pilot routes in Ireland, a radio coverage planning exercise was undertaken to optimise the deployment. Several technical and practical factors were taken into consideration during this exercise. This paper outlines Transport Infrastructure Ireland’s C-ITS pilot infrastructure, discusses the key technical and practical factors taken into consideration and summarises the results achieved. As a result of this radio coverage planning exercise, we were able to achieve over 50% cost savings on infrastructure deployment for C-ITS services via ITS-G5 technology on the M50 and M1 motorways.

Vehicles have been connected for decades through over-the-air updates, infotainment systems, Wi–Fi, and Bluetooth interfaces. The emergence of cooperative intelligent transport systems (C-ITS) as the next generation of intelligent transport systems (ITS) has greatly expanded this connectivity. C-ITS is a key enabler for autonomous driving, providing real-time coordination between vehicles and infrastructure. By linking vehicles, infrastructure, road users, and cloud platforms through vehicle-to-everything (V2X) communication, C-ITS supports safer, more efficient, and intelligent transport. At the same time, expanded connectivity enlarges the cyberattack surface, exposing safety functions such as braking, steering, and traffic signal control to potential manipulation. This chapter contributes a literature-based conceptual mapping that uses a tri-level perspective to relate vulnerabilities to detection techniques. It pinpoints technical challenges, including false positives, latency, scalability, heterogeneity, and adversarial machine learning, and discusses strategies such as hybrid intrusion detection systems (IDS). To further enhance applicability, it presents methodologies including simulations, hardware-in-the-loop testbeds, and case studies based on C-Roads implementations. It further incorporates cybersecurity asset management and cybersecurity risk assessment as part of the C-ITS ecosystem, consistent with the NIST Cybersecurity Framework (CSF) and ISO/SAE 21434 standard, to enforce regulation and lifecycle-dependent security administration. The chapter also highlights the research challenges, which include asset-agnostic detection, insider and supply-chain threats, adversarial resilience, regulation alignment, and few real-world verifications, and provides a roadmap to further develop IDS research and practice toward C-ITS.

Cooperative, connected and automated Intelligent Transportation System (ITS) aims to improve road traffic safety, comfort, security, and traffic management by sharing data/information among vehicles, infrastructure and road users. Cooperative ITS (C-ITS) also targets the reduction of environmental impact by the road transportation systems. Regarding self-driving vehicles, C-ITS plays a complementary role for enhancing on-board vehicle sensory data and thus making autonomous vehicles more robust and safe. In this paper we present the C-ITS platform, the test-cases and scenarios to be performed during the Lisbon Pilot of the AUTOCITS project. We also provide a description of the C-ITS Day-1 services, as simulated and also real-world events, that will be deployed and evaluated during the test-cases. Conventional, instrumented and autonomous vehicles will take part on the Lisbon Pilot, all equipped with on-board connected vehicular technologies. Two scenarios will be considered, motorway and urban-node scenarios, having distinct vehicles and test-cases, but all sharing the same C-ITS technology framework.

Intelligent Transport Systems (ITS) and Cooperative Intelligent Transport Systems (C-ITS) are of high significance, mainly due to the benefits they have in terms of operation of the transport network. Despite ITS and C-ITS importance in the operation of the transport network, there is a major knowledge gap regarding their development, way of operation and significance worldwide and especially among the responsible for their deployment public authorities. In order for such fragmentations to be tackled, an online training platform concerning the operation and impacts of ITS and C-ITS has been designed in the framework of the European Union's Horizon 2020 funded CAPITAL project. For the proper design of the CAPITAL Online Training Platform, two main approaches have been studied: capacity building and massive open online courses. The present study provides insight regarding the design and the context of the CAPITAL Online Training Platform.

Intelligent Transport Systems (ITS), and the specialised subset of them represented by Cooperative ITS (C-ITS), are part of the wider machine-to-machine communications, and software driven world. The aims of ITS and C-ITS are multifold but for the purposes of this book are mainly in improving safety of transport users and their transport modes. The chapter that follows covers some of the basics of security, including the contrast with safety and privacy, the role of privacy protection in the ITS context, and a review of the cryptographic countermeasures recommended for C-ITS using the certificate and signature schemes defined in IEEE 1609.2.

Today, more and more vehicles on the road are being connected via roadside communication networks for a wide range of services, ranging from road safety and navigation applications to in-vehicle entertainment. Cooperative Intelligent Transport Systems (C-ITS) services are aiming to improve road safety, traffic management and environmental performance of road transportation by sharing data between vehicles and road operator’s infrastructure via roadside communication networks. This paper investigates the capacity requirements of roadside ITS-G5 and cellular communication networks for supporting the emerging C-ITS applications, based on the C-ITS pilot implemented by Transport Infrastructure Ireland (TII). The results indicate that although the current communication networks are able to support the capacity demand of C-ITS applications at a low penetration rate of C-ITS equipped vehicles during the early uptake of C-ITS, new measures are required to improve the capacity of roadside communication networks when the number of C-ITS applications or the penetration rate of C-ITS equipped vehicles on the road goes high.

Cooperative Intelligent Transportation Systems (C-ITS) enable vehicles to be aware of objects that are not in line of sight, by interacting directly with each other and the surrounding road infrastructure. C-ITS promise to reduce traffic congestion, lessen the environmental impact of transportation, and most importantly, significantly reduce the number of (lethal) traffic accidents. As C-ITS is subject to strong network effects, adoption of C-ITS is a key driver of its (societal) benefits. Therefore, this paper estimates penetration rates of C-ITS equipped cars in the car park of Flanders, Belgium. Based on the preferred policy option with mandatory adoption, as proposed in the recent new Delegated Regulation of the European Commission, full penetration of C-ITS in the Flemish car park is expected later than twenty years after the mandate enters into force. Determination of C-ITS adoption numbers is valuable for a number of stakeholders, such as national and local governments, road authorities, technology providers and network operators. Additionally, penetration numbers allow Member States to ascertain to what extent C-ITS can contribute to the goal of zero traffic mortalities by 2050, as envisioned by the European Commission.

During the past few decades, the presence of global navigation satellite systems (GNSSs) such as GPS, GLONASS, Beidou and Galileo has facilitated positioning, navigation and timing (PNT) for various outdoor applications. With the rapid increase in the number of orbiting satellites per GNSS, enhancements in the satellite-based augmentation systems (SBASs) such as EGNOS and WAAS, as well as commissioning new GNSS constellations, the PNT capabilities are maximized to reach new frontiers. Additionally, the recent developments in precise point positioning (PPP) and real time kinematic (RTK) algorithms have provided more feasibility to carrier-phase precision positioning solutions up to the third-dimensional localization. With the rapid growth of internet of things (IoT) applications, seamless navigation becomes very crucial for numerous PNT dependent applications especially in sensitive fields such as safety and industrial applications. Throughout the years, GNSSs have maintained sufficiently acceptable performance in PNT, in RTK and PPP applications however GNSS experienced major challenges in some complicated signal environments. In many scenarios, GNSS signal suffers deterioration due to multipath fading and attenuation in densely obscured environments that comprise stout obstructions. Recently, there has been a growing demand e.g. in the autonomous-things domain in adopting reliable systems that accurately estimate position, velocity and time (PVT) observables. Such demand in many applications also facilitates the retrieval of information about the six degrees of freedom (6-DOF - x, y, z, roll, pitch, and heading) movements of the target anchors. Numerous modern applications are regarded as beneficiaries of precise PNT solutions such as the unmanned aerial vehicles (UAV), the automatic guided vehicles (AGV) and the intelligent transportation system (ITS). Hence, multi-sensor fusion technology has become very vital in seamless navigation systems owing to its complementary capabilities to GNSSs. Fusion-based positioning in multi-sensor technology comprises the use of multiple sensors measurements for further refinement in addition to the primary GNSS, which results in high precision and less erroneous localization. Inertial navigation systems (INSs) and their inertial measurement units (IMUs) are the most commonly used technologies for augmenting GNSS in multi-sensor integrated systems. In this article, we survey the most recent literature on multi-sensor GNSS technology for seamless navigation. We provide an overall perspective for the advantages, the challenges and the recent developments of the fusion-based GNSS navigation realm as well as analyze the gap between scientific advances and commercial offerings. INS/GNSS and IMU/GNSS systems have proven to be very reliable in GNSS-denied environments where satellite signal degradation is at its peak, that is why both integrated systems are very abundant in the relevant literature. In addition, the light detection and ranging (LiDAR) systems are widely adopted in the literature for its capability to provide 6-DOF to mobile vehicles and autonomous robots. LiDARs are very accurate systems however they are not suitable for low-cost positioning due to the expensive initial costs. Moreover, several other techniques from the radio frequency (RF) spectrum are utilized as multi-sensor systems such as cellular networks, WiFi, ultra-wideband (UWB) and Bluetooth. The cellular-based systems are very suitable for outdoor navigation applications while WiFi-based, UWB-based and Bluetooth-based systems are efficient in indoor positioning systems (IPS). However, to achieve reliable PVT estimations in multi-sensor GNSS navigation, optimal algorithms should be developed to mitigate the estimation errors resulting from non-line-of-sight (NLOS) GNSS situations. Examples of the most commonly used algorithms for trilateration-based positioning are Kalman filters, weighted least square (WLS), particle filters (PF) and many other hybrid algorithms by mixing one or more algorithms together. In this paper, the reviewed articles under study and comparison are presented by highlighting their motivation, the methodology of implementation, the modelling utilized and the performed experiments. Then they are assessed with respect to the published results focusing on achieved accuracy, robustness and overall implementation cost-benefits as performance metrics. Our summarizing survey assesses the most promising, highly ranked and recent articles that comprise insights into the future of GNSS technology with multi-sensor fusion technique.

As location-aware IoT (Internet of Things) devices intend to spread across almost every application, IoT devices are going to require different GNSS (Global Navigation Satellite System) receiver design requirements. To deliver on IoT opportunities, system architects and designers need to look carefully at every aspect of system design and architecture to be able to enable the full potential of the IoT era. IoT devices are equipped with different connectivity sensors that can provide a reliable integrated location-awareness solution. IoT devices are mainly relying on GNSS chip to deliver a service when no other RF signal is available. Hence, GNSS chip is expected to maintain a robust and reliable performance even in harsh environment. The harsh environment includes multipath environment, NLOS (Non-Line of Sight) GNSS signal reception, weak GNSS signal power reception, superimposed motion (e.g., arm swing, floating platform fluctuation …etc.), operation under freezing temperature, and interference. The targeted low-cost system design along with the expected harsh operational environment derived us to face exceptional GNSS signal reception condition. This paper discusses lessons learned, and challenges faced signal acquisition, reacquisition, and tracking for several IoT applications. Different operational modes are also explored to accommodate various use cases scenarios for several IoT applications. These different power consumption operational modes allow the GNSS chip to achieve an Ultra-low power consumption which indeed required for IoT applications. This paper provides an overview about the addressed GNSS chip fabricated in a 28nm FD-SOI process. We demonstrate a system performance and power consumption comparison of this chip along with other state-of-the-art GNSS chip. We compare the positioning performance for this device alongside another state-of-the-art GNSS modules and Novatel’s tightly coupled GNSS/INS (Inertial Navigation System) reference solution at numerous scenarios ranging from walking, running, and driving at several sites under different environment.

Urban environments present major challenges to the global navigation satellite system (GNSS) positioning to estimate the position of a self-driving vehicle. GNSS signals are often obstructed by buildings and other obstacles, leading to reflected and diffracted signals, which cause major positioning errors and known as line-of-sight (LOS) and non-line-of-sight (NLOS) multipath errors. Particularly, real-time kinematic (RTK)-GNSS, which can estimate the position in the centimeter-level accuracy, cannot estimate the precise position when there exist NLOS multipath errors. If we use NLOS GNSS code and carrier phase multipath measurements into the RTK-GNSS algorithm, carrier phase integer ambiguity cannot be solved, or wrong integer ambiguities are estimated. As a result, integer ambiguity resolved to fix solutions cannot be estimated, or wrong fix solutions which have large position error are estimated. That is a major barrier to using RTK-GNSS in autonomous driving. In this paper, we propose a novel NLOS detection and exclusion technique to enhance existing RTK-GNSS positioning in urban areas using only GNSS measurements alone. The key idea behind the proposed technique is that the NLOS pseudorange error will always be a positive value. NLOS pseudorange is longer than the true range, which is the distance between the GNSS antenna and satellite, due to the time delay caused by reflection and diffraction. The distribution of the NLOS error is not along with a Gaussian distribution. We use this phenomenon to detect the NLOS signals. We propose a technique to detect and eliminate the NLOS multipath using double-differenced pseudorange residuals (DD-PR). In the RTK-GNSS, DD GNSS measurement between the rover and base station is typically used. Here, we compute the DD-PR to detect the NLOS signals. In order to compute the DD-PR, the true position of the vehicle must be determined in advance. To solve this contradiction, we use a particle filter. The particle filter generates the position hypotheses, which is called as “particle”. These particles are used as the position candidate for estimating the DD-PR. When the DD-PR can be calculated at each particle position, we detect the large positive errors in DD-PR as NLOS signals. Then we compute the integer ambiguity of only the LOS DD carrier phase to estimate the precise vehicle position. The flow of the proposed technique is as follows: (1) The hypothetical user positions are created as particles, where the state vectors of these particles are 3D positions, x, y, and z. The initial particles are distributed based on the solution of the normal GNSS point positioning and its variance. (2) In the prediction step of the particle filter, the particle state in the next time step is estimated using the velocity of each particle computed from GNSS Doppler measurements. (3) Next, we compute the DD-PR at each particle location. The receiver clock bias, satellite clock bias, ionosphere error, and troposphere error in GNSS pseudorange are canceled through DD computation. As a result, DD geometrical range between receiver and satellite and DD multipath errors remain in the DD measurement. We compute DD-PR using the particle position. As a result, the only multipath error remains if the particle is close to the true position. NLOS multipath error is always positive value; we generate the subset of the LOS measurement hypothesis at each particle using a simple threshold test. (4) Next, we compute the RTK-GNSS position solution using the generated subset of the LOS measurement hypothesis at each particle location. It is expected that the RTK-GNSS fix solution can be calculated if the particle location is close to the true position because the NLOS signal has been eliminated. By contrast, if the particle is far from true position, the integer ambiguity cannot be solved because of using the incorrect subset of LOS measurements. The likelihood of each particle is computed from a distance between the particle location and the RTK-GNSS fix solution. The particle that is closest to the true position has a higher likelihood than the other particles. The probabilistic weight of each particle is then updated based on its likelihood. (5) Finally, all the particles are resampled based on their new weights. Particles that are sufficiently close to the true position survive this resampling step, and the average position of the remaining particles are employed as the final estimated user position. To confirm the effectiveness of the proposed technique, a kinematic positioning test was performed in Shinjuku, Tokyo, Japan. From the several positioning tests, the proposed technique could dramatically improve the availability and accuracy of RTK-GNSS fix solutions in the dense urban canyon where there exist a lot of NLOS multipath signals.

There is a critical need for research in proactive and predictive management of the resilience of transportation systems implementing new technologies. Cooperative Intelligent Transportation System (C-ITS) uses wireless technology to allow vehicles and infrastructure to talk to each other in real-time. This makes it easier for people to work together on the road and makes it possible to make safer and more efficient traffic flows. Significant progress may be made in the transportation industry as a result of the incorporation of self-powered sensors into C-ITS providing resilience in transportation operation. One advantageous feature is that these sensors, which generate their power, could be deployed in a variety of C-ITS implementation scenarios. To assist decision-makers in making the most informed choice possible concerning investments and implementations, a type-2 neutrosophic number (T2NN) based VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) method is used to perform advantage prioritization. To accomplish this goal, a case study is carried out to determine which of the three alternatives is the most suitable based on a set of twelve criteria that is divided into four aspects. According to the findings, the applicability and short-term benefits are the most crucial factors in determining which option is the most advantageous for the use of self-powered sensors in C-ITS. This is because both of these factors have an immediate impact on the system.

Cooperative Intelligent Transportation Systems (C-ITS) is one of the most prominent solutions to facilitate many new exciting applications concerning road safety, mobility, environment, and driving comfort. This technology is now on the verge of actual deployments. However, security threats, privacy, and trust management remain the most significant concerns. C-ITS relies highly on node cooperation and trust as vehicles take the decision based on the information received from the roadside network infrastructure. This information should be accurate and reliable to ensure proper functioning of the system. However, the presence of a misbehaving or compromised node in the system can lead to catastrophic results for both safety and traffic efficiency. It is therefore essential to detect misbehavior and defend the C-ITS against it. Although various studies have proposed misbehavior detection at the vehicular plane, the study that explores the machine learning capabilities to detect misbehavior at infrastructure plane is not present. Thus, in this paper, we propose a solution to detect misbehavior at the infrastructure plane of C-ITS that employs the predictive capabilities of Deep Learning. We compare the performance of Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) models of Deep Neural Network.