Fifth Generation (5G) networks and beyond are envisioned to provide user-focused communications, supporting diverse services with enhanced Quality of Service (QoS). Pivotal to this evolution is user equipment, which is increasingly performing advanced computational tasks beyond the edge of the network, known as the Extreme-Edge. Seamless integration of Extreme-Edge devices (EEDs) into the 5 G framework is however hindered, due to challenges in terms of device management, resource restrictions and interoperability issues. To address these barriers, we realize the Extreme-Edge Orchestrator (EEO), a management and orchestration framework enabling the extension of the 5 G cloud-to-edge continuum towards the Extreme-Edge. The EEO enables real-time resource monitoring and lifecycle management of network applications, including Artificial Intelligence/Machine Learning (AI/ML) tasks, deployed on EEDs. Unlike existing theoretical studies, our solution is deployed on an operational research-center-wide 5 G testbed and evaluated using an AI/ML-based network QoS prediction application, in the automotive domain. Our results show that the EEO supports efficient resource utilization, dynamic EED selection under device mobility scenarios and maintains robust service performance under computational stress-demonstrating its capability to support next-generation network services.

Telecommunications are undergoing a transformation due to the introduction of 5 G and the upcoming 6G technologies, which promise previously unheardof speeds, extremely low latency, and widespread access. The resource management of edge, fog, and cloud infrastructures is facing new difficulties as a result of these improvements. In order to optimize performance, cost-efficiency, and energy consumption, this paper investigates how machine learning (ML) might be used to improve resource allocation to services across this continuum, specifically in the context of B5G and 6G networks. In this work, we propose a reinforcement learning based algorithm using Q-learning for determining the best locations of services across the 6 G edge to cloud continuum. The aim is to ensure better QoS to the users and efficient use of network resources. To evaluate the performance of the proposed approach we compare it with a traditional state of the art algorithm in the context of content placement in CCNs, taking into consideration network related metrics such overall network traffic and delay. The experimental analysis shows that the proposed algorithm performs better than the traditional state of the art algorithm in most cases and reveals some insights on how network topological configurations affect the performance.

Advanced Threat Intelligence Orchestrator (ATIO) is a sophisticated middleware solution designed to enhance unified threat management (UTM) monitoring processes by adhering Security Orchestration Automation Response (SOAR) capabilities. This paper provides a detailed overview of ATIO, highlighting its multitasking capabilities towards coordinating information from different types of tools, usually bringing with them different types of data. Also, it gives some details on the system implementation and some indicative operational workflows. Central to ATIO's functionality is its ability to concurrently or sequentially automate the execution and processing steps of multiple workflows, while adhering to cyber security standards, organization policies and regulations. The design of ATIO is flexible, accommodating various interconnected services and tools to meet specific requirements, as well as diverse infrastructure interfaces, accommodating different specifications seamlessly adhering standardized formats and Cyber Threat Information (CTI) languages, such as STIX2.1. This integration enhances interoperability and expands the scope of cyber-threat intelligence operations by enabling connectivity with various systems and diversified data types. Moreover, ATIO automation nature, boosting detection and acknowledge efficiency and responsiveness in threat intelligence operations. It enables users to alter and filter workflow steps, preparing information for correlation and tracking cyber threat information (CTI) effectively. Additionally, ATIO includes robust mechanisms for monitoring user actions within the system, ensuring accountability and providing valuable insights into operational activities.

The advent of 5G and beyond systems is expected to shape the automotive vertical, as safety-critical vehicular applications rely on the network to meet their stringent Quality of Service (QoS) requirements. Predictive QoS (pQoS) has been proposed as a mechanism that allows automotive applications to proactively adapt in view of forthcoming QoS changes. Although pQoS is typically facilitated via classical (centralized) Machine Learning (ML) methods, the demand for data privacy has led to the emergence of distributed ML schemes. Efficient training of ML models however requires large volumes of (kinematic-state and connectivity) QoS data, so as to capture the involved spatio-temporal effects.To that end we hereby present and publicly share NordicDat, a QoS dataset collected during a two-week measurement campaign, driving across three European countries. NordicDat contains over 90K samples of physical layer, network and mobility-related features. Contrary to prior works, it includes multiple instances of cross-boarder roaming, diverse vehicle speed profiles and radio access technologies (generations). Further, we provide a thorough NordicDat data analysis, highlighting the dependencies between the NordicDat’s features and the resulting QoS values (throughput, delay). To showcase its broad usability, we train pQoS ML models over NordicDat in classical and distributed fashion. Our results demonstrate for the first time the viability of distributed pQoS with real-word data, which achieves similar (within a margin of 10%) accuracy to that of classical ML, cropping privacy-preserving benefits.

Inland water level and its dynamics are key components in global water cycle and land surface hydrology, significantly influencing water resource management, biodiversity, and climate change impacts. Space-based altimetry observations of inland water surface elevation is an important data source to supplement the limited in-situ measurements. In this study, the evaluation of Sentinel-3 and Sentinel-6 MF derived water levels was conducted over Douro and Danube rivers. However, it is still very challenging to obtain accurate water level measurements over narrow rivers, with width less than 300m. Gauge measurements were utilized for the validation of altimetry-derived water levels. Two outlier detection methods were applied for data pre-processing to improve the accuracy of measurements. The root-mean-square-error (RMSE) of the Sentinel-6 mission over Danube River is 0.57m, while the mean-RMSE of the Sentinel-3 over Douro River is 2.12m.

The THETIDA project addresses climate change threats, utilizing a holistic approach to safeguard Europe’s coastal and underwater cultural heritage. Employing environmental modeling for specific climate change scenarios, the project conducts quantitative and qualitative impact assessments, combining environmental and pollution analyses at seven pilot sites across Europe. The developed and deployed technology includes sensors, satellite image processing, smart buoys, AUVs, wearables and crowdsourcing applications supported by ocean forecasting and hazard mapping services. The obtained datasets inform adaptive strategies through a Decision Support System with enhanced visualization capabilities and aim to support sustainable plans for cultural heritage preservation and resilient, climate-neutral policies.

Climate change-induced factors, including sea level rise, ocean acidification, intensified storms, temperature elevation, and coastal erosion, endanger the stability and conservation of underwater and coastal cultural heritage (CH). Despite the critical role of maritime heritage in understanding past culture and history, there is a shortage of long-term sensing data for the ongoing assessment of key environmental parameters affecting underwater and coastal monuments. This study focused on the development of underwater and coastal sensor devices aiming to provide long-term datasets for enhanced assessment of CH sites, which coupled with remote sensing data can enhance the understanding of underwater parameters and contribute valuable insights into climate-related phenomena in underwater and coastal CH sites.

Real-time environmental monitoring in desert areas is often particularly challenging. This paper presents a sophisticated Wireless Sensor Network system comprising WSNs and communication gateways and creating a versatile Internet of Things (IoT) and Satellite Internet of Things (SatIoT) solution adaptable to diverse environmental conditions, including deserts. The WSNs are strategically deployed to measure environmental parameters, while communication gateways collect and transmit the gathered data to a cloud-based data management platform. Multiple communication protocols, including Bluetooth Low Energy (BLE), LoRaWAN, Wi-Fi, LTE, and satellite interfaces, ensure flexibility and adaptability to different deployment scenarios and geographical locations. The transmission algorithm prioritizes network connectivity availability, enabling robust, reliable and stable data transmission to the cloud. This comprehensive system facilitates real-time monitoring, historical trend analysis, and insights into air quality conditions. The low-cost WSNs, with their compact design and energy-efficient features, offer an affordable and valuable tool for widespread deployment in environmental monitoring applications.

Mixed traffic of conventional and automated vehicles entails many challenges concerning the safe coexistence and the improvement of traffic efficiency. As a solution, a Traffic Management Center (TMC) combines information from physical sensors and communicated data to enable advanced traffic controllers. Since there is no sufficient penetration rate of automated vehicles nowadays, such a solution could be developed and tested with synthetically generated data using simulation, which models the future Intelligent Transportation System (ITS). This paper addresses the challenges for evaluation of such a system by the means of simulation. For this purpose, we present the Eclipse MOSAIC Multi-Domain Simulation Framework, which provides the possibility to couple best-in-class simulators of the domains traffic, application, and communication, as well as to include further models and external native code libraries. Moreover, we discuss modeling extensions for traffic control algorithms and our method to create a highway scenario based on real traffic data. As primary result, many research questions for mixed traffic and ITS could be investigated using the MOSAIC framework. In an exemplary study, we analyze the deployment effort for infrastructure and communication technologies in order to facilitate a Mainstream Traffic Flow Controller in a realistic scenario with mixed traffic. Simulations performed with MOSAIC confirmed that traffic efficiency improvement is feasible even with fewer infrastructure components, thus with less cost.

This work aims to provide an overview of the Mixed Reality (MR) technology’s use in maritime industry for training purposes. Current training procedures cover a broad range of procedural operations for Life-Saving Appliances (LSA) lifeboats; however, several gaps and limitations have been identified related to the practical training that can be addressed through the use of MR. Augmented, Virtual and Mixed Reality applications are already used in various fields in maritime industry, but its full potential has not been yet exploited. SafePASS project aims to exploit MR advantages in the maritime training by introducing a relevant application focusing on use and maintenance of LSA lifeboats. An MR Training application is proposed supporting the training of crew members in equipment usage and operation, as well as in maintenance activities and procedures. The application consists of the training tool that trains crew members on handling lifeboats, the training evaluation tool that allows trainers to assess the performance of trainees and the maintenance tool that supports crew members to perform maintenance activities and procedures on lifeboats. For each tool, an indicative session and scenario workflow are implemented, along with the main supported interactions of the trainee with the equipment. The application has been tested and validated both in lab environment and using a real LSA lifeboat, resulting to improved experience for the users that provided feedback and recommendations for further development. The application has also been demonstrated onboard a cruise ship, showcasing the supported functionalities to relevant stakeholders that recognized the added value of the application and suggested potential future exploitation areas. The MR Training application has been evaluated as very promising in providing a user-friendly training environment that can support crew members LSA lifeboat operation and maintenance, while it is still subject to improvement and further expansion.

The convergence of Machine Learning (ML) with the edge computing paradigm has paved the way for distributing processing-heavy ML tasks to the network's extremes. As the edge deployment details still remain an open issue, distributed ML schemes tend to be network-agnostic; thus, their effect on the underlying network's resource consumption is largely ignored.In our work, assuming a network tree structure of varying size and edge computing characteristics, we introduce an analytical system model based on credible real-world measurements to capture the end-to-end consumption of ML schemes. In this context, we employ an edge-based (EL) and a federated (FL) ML scheme and in-depth compare their bandwidth needs and energy footprint against a cloud-based (CL) baseline approach. Our numerical evaluation suggests that EL exhibits a minimum of 25% bandwidth-efficiency compared to CL and FL, if employed by a few nodes higher in the edge network, while halving the network's energy costs.

The proliferation of machine learning (ML) applications has lately witnessed a considerable shift to more distributed settings, even reaching hand-held mobile devices; there, contrary to typical Centralized learning (CL) whereby the involved (large amounts of) training data are centrally gathered to train models, the load of training tasks is distributed across a set of capable mobile learners at the expense of their own energy. The idea of Federated learning (FL) has emerged as a privacy-preserving mechanism suggesting that the ML model parameters rather than data, are sent over the network to a central point of aggregation. However, when relaxing the privacy concerns, the debate strongly relates to the available network resources. Interestingly, the sofar theoretical or even experimental comparison of the two approaches overlooks network conditions and remains of low realism. In this work we rely on past measurement studies to introduce a realistic system model that accounts for all involved mobile network conditions such as bandwidth and data availability (af-fecting training accuracy and model aggregation) as well as user mobility patterns (affecting data loss). A dedicated simulation framework we have developed replays rich mobile-traces allowing for a comprehensive comparison of the two ML approaches over a large set of training data shedding light on network-resources utilization, energy efficiency and training convergence. Intuitively, our results suggest that the ratio between the employed raw data and the corresponding ML model shapes the conditions under which FL acts as a network-efficient alternative to CL. Interestingly enough, asymmetry in data availability across users as well as their varying number are shown to hardly affect the FL approach in traffic and energy needs, pointing both to its promising potential and the need for further research.

A Connected Vehicle System (CVS) is a cyberphysical system of highly-equipped infrastructure-connected vehicles interconnected with road-side units and cloud-based services to offer safer driving. Despite the ever increasing relevant research, the testing of CVS functionalities and communication features remains problematic; computer-based simulation requires detailed system models while field-testing is expensive, focusing on few components and may raise safety concerns. In this paper, we present a full CVS prototype testbed enabled to realize a broad set of timely Vehicle-to-Infrastructure (V2I) use-cases and serve as the basis for automotive cybersecurity testing. The testbed designed and built in the context of the H2020 SAFERtec project, is described in terms of its hardware requirements, software design and implementation. The conducted testing activities on its capability to realize the considered V2I use-cases and support cybersecurity testing is explained. More importantly, the paper details take-home lessons derived from the CVS implementation experiences aiming to assist future development of automotive prototype testbeds.

The proliferation of mobile devices with ubiquitous Internet access has made content access patterns highly volatile and spatio- temporally varying. At the same time, virtualization techniques have enabled the emergence of virtual Content Delivery Network (vCDN) providers, that bundle together a virtual infrastructure by utilizing storage resources anywhere along the path between the access network and the corresponding data center, where the requested content actually resides. In this context, efficient content placement in the various storage layers depends on accurately estimating content access patterns from the different access networks at any given time. At the same time, different vCDN providers compete against each other to provide low content retrieval latency to their users. However, there are some interesting synergies that might emerge among otherwise competing vCDN providers: (i) host content of another vCDN provider and, (ii) provide own content to the users/customers of other vCDN providers. In this paper, we formulate the overall problem of content placement for multiple vCDN providers that employ collaboration as an overall social-welfare maximization problem. The solution to this problem gives the optimal placement, achievable only in the case of full information. Alleviating the need for full information and considering vCDN providers separately as profit seekers, we also devise a distributed algorithm for content placement by exchanging limited information among them. Extensive simulation experiments show that business collaboration among competing vCDN providers is beneficial, as compared to isolated offerings, and allows them to adapt faster to content pattern changes.

In this paper, a novel model for the cyber-security analysis of Level 3 (L3) Automated Driving (AD) systems is proposed by integrating aspects of functional safety. The model is built based on the state-of-the-art framework for cyber security analysis, known as Threat Analysis and Risk Assessment (TARA), which quantifies the likelihood and the impact of attack and combines them in order to derive an attack risk value. The novelty lies in the bespoke integration of the impact calculation, which incorporates the notion of controllability of an attack by the AD system and/or by the driver. The proposed model is applied for the Urban Chauffeur and the Highway Chauffeur AD system functions, providing insights into the security risk in a wide area of distinct operational design domains as defined by SAE J3016. Remote attack surfaces (e.g., modifications of road infrastructure) are also taken into account in the analysis.

The tomorrow's elastic adaptive mobility industrial research project developed eleven collaborative mobility apps addressing various traffic issues and scenarios. The apps, involving different aspects and degrees of collaboration (e.g., direct user participation, shared objectives, coordination, etc.), aim at increasing the driver/traveler awareness and support a better behavior. This paper describes the apps and the underlying system architecture shared by the participating car manufacturers. Then, it provides a user acceptance analysis grouping the apps according to the three main types of users and stakeholders: drivers, travelers, and administrators/operators. Data, collected in five European countries, shows that acceptance and expected impact are positive. The actual road-test experience did not diminish the high expectations raised by an initial presentation on paper, showing a good maturity of the prototypes. The Administrator app cluster shows a slightly better assessment, highlighting the importance of considering collaborative mobility as a system, including road, infrastructure, and traffic management.

ICT advances in emerging domains such as Internet of Things (IoT), Augmented Reality/ Virtual Reality (AR/VR), big data analytics, cyber-physical systems and cloud computing have revolutionized and boosted the creation of cyberworlds as information spaces that allow us to augment the way we interact with each other and with the physical world. Naturally, other than businesses cyberworlds can benefit modern hyper connected societies at their entirety (transport, mobility, health, smart living, etc.) and further to that the physical world around us can also be part of such process. In the present paper the focus is given on citizen-powered cyberworlds for Environmental Monitoring which are created from crowdsourced observations engaging, through gamification, citizens and communities. The means of engagement include serious gaming, collection of geo-tagged IoT, such as images, video and sensor measurements as well as management and storage of diverse IoT as OGC compliant observations all conveyed into a dedicated information space.

When a disastrous event occurs and an urban area suffers from collapsed buildings, Urban Search and Rescue (USaR) teams require a fast and thorough building damage assessment, to focus their rescue efforts accordingly. After the evaluation process, their observations/results have to be transferred to their base (center of operation - COP) for further assessment and orchestration of the survival extraction actions. In this work, a communication scheme that can be used in emergency situations is introduced. The proposed solution doesn't depend on existing functional cellular networks such as GSM, 3G, 4G, etc., neither assumes Wi-Fi connectivity to the Internet, since in disaster scenarios they could be unavailable or they could have large interruptions and delays. On the contrary, we propose a low-cost solution that is easy to build and deploy and is modular to expand from a small area to a large part of an urban setup, based on the needs of the USaR activities. Particularly, a wireless mesh-network is designed to cover the needs of the first responders in the actual disaster area, whereas a long-range wireless network is designed to transfer the data securely back and forth to the center of operation.

Security assurance is defined as the degree of confidence that the security requirements of an IT system are satisfied. In view of the emerging paradigm of connected vehicles i.e., dynamic Cyber-Physical systems of highly-equipped infrastructure-connected vehicles, specifying the involved assurance becomes highly-critical yet challenging; vehicles increasingly exploit various communication means to exchange rich data of relevance with the infrastructure resulting in a large attack surface. Both the complexity and uncertainty are increased rendering the so-far generic methods for security assurance costly-to-apply. In this position paper we introduce a security assurance framework tailored for connected vehicles, as explored by the EU-funded H2020 SAFERtec project. We put under the microscope two instances of vehicle-to-infrastructure communications and relying on an innovative modeling methodology we identify the involved security and privacy requirements. We then present the way to enhance the processes of the credible yet generic Common Criteria approach to gain evidence that the above requirements are met. The experimental evaluation of the framework is carried-out over a reference implementation of a prototype vehicle connected to road-side units and cloud-based services. The expectations are that our work assists to effectively construct assurance arguments increasing trust in connected vehicles.

Present public transport services still suffer from issues such as time deviations from the static timetable, overcrowded buses, increased on board time, and long wait at bus stops. This work studies the experimental implementation of an on-demand public bus transportation service in Trikala, a medium-sized Greek city. With a view to optimize the service from both the operator's and the citizens' point of view, this paper presents an insertion heuristic solving the static multivehicle dial-a-ride problem with time windows and a fixed fleet of vehicles. Since viability of such a service depends on its ability of involving a significant number of users and getting reliable information, we tested a gamification layer, aimed at motivating public transport users to participate and behave correctly with the system. We present and discuss a novel pervasive computing architecture and various types of serious games designed to achieve these goals. We finally report early usability test results and some simulation-based indications on the design of city-scale deployable serious games to enhance public transport-based mobility.

Public attitudes are a key factor shaping the demand and market for autonomous vehicles. Previous studies are limited to a priori attitudes since such vehicles are not yet fully accessible to the public. A survey among 200 passengers of autonomous mini buses in real operation in the city of Trikala shows that they were well accepted and integrated in the people's everyday life and that there were no major concerns as regards safety and security on board. A survey involving 519 adult Trikala citizens revealed that regular users of the autonomous mini bus were younger people, experienced with driving automation systems, who would prefer fully autonomous than manual driving and who would use an autonomous vehicle. Although the findings should be verified with bigger samples and in more cities and traffic environments, they are encouraging towards the introduction of autonomous buses in urban environments.

Emergency situations, such as fire incidents or terrorist attacks, have imposed strongest safety requirements for crowds gathering. The enhancement of situation awareness, decision support and emergency notification systems that facilitate the safety and effectiveness of evacuation processes in a major incident is therefore of utmost importance. ICT, IoT and smart systems have gained much attention in many industries and application domains in the latest years as they can play a significant role by introducing novel systems and applications for intelligent, safe and fast evacuation of people. This work focuses on the development of a communication gateway that can support and improve emergency notification systems by providing appropriate, updated and accessible information to operators and people in affected zones in a reliable way. The presented approach uses an adaptable data model that can be applied to evacuation systems consisting of heterogeneous components, such as legacy systems, novel devices, sensors and actuators. The proposed communication gateway follows the publish/subscribe communication standard for real-time systems and is built on Data Distribution Service (DDS) of the Object Management Group (OMG). The evacuation systems are hereby transformed into interoperable, distributed, scalable, robust, and real-time IoT infrastructures.

Stereo-matching is an indispensable process of dense 3D information extraction for a wide range of applications. Relevant methods rely on cost functions and optimization algorithms for estimating accurate disparities. This work analyses a novel cost for stereo matching under radiometric differences in the stereo-pair, which is based on a modification of the widely used census transformation. It is proposed to define the census on image x and y gradients. The modified census (MC) on gradients is evaluated as an independent matching cost in the presence of severe radiometric differences. For this, the original and the modified census transformation (CT) are implemented in three different aggregation schemes, namely fixed rectangular windows, adaptive cross-based support regions and semi-global matching. It is shown that the MC can provide better results in the cases of local radiometric differences, such as different illumination conditions. Thus, this approach can extend the inherent capability of the original CT to address global monotonic radiometric differences.

Dynamic charging of electric vehicle technology aims to deploy the use of electric vehicle on a large scale. In order to realize and achieve an effective and reliable system, the information and communication solutions (ICT) need to be developed. The ICT solutions look particularly to assess areas such as routing, booking of the charging lane, billing, vehicle identification, guidance to charging lane and assistance while charging. In this paper, we review the state of the art of ICT solutions and provide indications for the future research.

This paper presents a centralized algorithm that targets at balancing energy supply to demand by controlling the charging power of EVs. Such a capability enables demand side management and facilitates a smoother integration of charging infrastructure to the grid. The algorithm targets charging management systems that handle multiple concurrent sessions in a deterministic manner It enables the creation of custom charging profiles according to user or system defined strategies and encompasses a mechanism that enables priotirization. The algorithm is designed to target the static charging case. However recommendations regarding the adaptations required to target the dynamic wireless charging case are reported.

Network-enabled services for public protection and disaster relief (PPDR) professionals have been more than necessary in today's emergency situations. Under the extreme circumstances of an emergency, it is essential to have networks which support the required data throughput as well as high availability in spite of high traffic volume, and which minimize the end-to-end delay for applications: however, the choice between the existing technologies is not so easy for PPDR entities, given the high number of parameters associated to satisfying the stringent PPDR requirements, high investments required to permit desirable availability as well as modernization of the existing services (i.e. voice and data) and design constraints posed by network providers as current deployed network reach their boundaries when emergencies occur. This paper analyzes three different emergency scenarios and then presents a subset of the results obtained in terms of financial and economic recommendations, along with technical reports on throughput and end-to-end delay.

Wireless communication technologies are critical for public protection and disaster relief (PPDR) professionals during the emergency operations that follow natural or man-made disasters, scenarios in which commercial terrestrial networks often fail to provide the necessary support. The reason is threefold: they simply get disrupted by the disaster, they cannot sustain the sudden surge of network demand or they fail to deliver the necessary bandwidth and/or other QoS guarantees. In every PPDR operation reliable voice communications are critical, especially in the very early stages of the response; nevertheless, there is an increasing demand from the PPDR community for a wider range of data-centric services. While current technologies used for PPDR operations provide a rich set of voice-centric services, they are unable to sustain high-bandwidth data-oriented applications. As the PPDR-TC EU consortium, we propose a hybrid approach to tackle the question of determining the future architecture for Pan-European PPDR networks based on the integration of Terrestrial and Satellite technologies, presenting our first simulation results on the integration of LTE and Satellite Networks.

Integration of wireless communications in vehicles presents the greatest challenge today in the field of vehicular safety, efficiency and comfort. Data fusion should be able to benefit from this additional source of information. This involves the association and fusion of received vehicle positions and other relevant data with the targets tracked by the onboard sensors' tracking systems. This paper describes a fusion approach for using wireless transmitted data in a vehicle perception system, with focus on data association and tracking methodology. The results were extracted by using two vehicles equipped with wireless communication hardware.

Vehicular ad hoc networks (VANETs) are in the heart of current and future automotive research. Most of the current vehicular safety applications are based on sensors installed on the vehicle, e.g., radars and laserscanners. Due to the evolution of wireless networks, there is a tendency to exploit the cooperation among vehicles to enhance road safety through the related applications. Path prediction of a driver's own vehicle and other vehicles is crucial for road safety. Path prediction can assist the driver in having an enhanced perception of the road environment and of the intention of other neighboring drivers. In this paper, an advanced cooperative path prediction algorithm is presented. This algorithm gathers position, velocity, acceleration, heading, and yaw rate measurements from all connected vehicles to calculate their future paths. In addition, map data with regard to the road geometry and, in particularly, the road curvature are used to enhance the path prediction algorithm. Comparative results of the path prediction, with and without wireless communications, are discussed. In addition, the algorithm is adapted for use in the emergency-electronic-brake-lights application. The results of this adaptation are also presented. This paper is another contribution in highlighting the advantages and, at the same time, the challenges of using communications among road users.

Preventive safety functions help drivers avoid or mitigate accidents. No quantitative methods have been available to evaluate the safety impact of these systems. This paper describes a framework for the assessment of preventive and active safety functions, which integrates procedures for technical performance, human factors, and safety assessments in one holistic approach. The concept of situational control , which is defined as the degree of control that a joint driver–vehicle system exerts over a traffic situation, has been introduced. Assessment involves technical evaluation , which assesses that the system and all subsystems work according to the functional and technical specifications, human factors evaluation, which assesses the behavioral situational control parameters, and safety potential evaluation, which performs a detailed assessment of internal and external impact mechanisms that change the situational control at the traffic level. The methodology has been applied to the INSAFES project, which integrates lateral and longitudinal control functions.

Research in the automotive field is currently focusing on exploiting the co-operation among vehicles to enhance road safety, efficiency and comfort. In vehicular ad hoc networks, especially in safety related applications, accuracy of the information exchanged through the wireless network, such as position and velocity of each vehicle, is crucial. For this reason a robust and accurate vehicular filter which also reduces measurements' noise is needed. The extended Kalman filter is used as standard technique for performing recursive nonlinear estimation. However, it provides only an approximation to optimal nonlinear estimation. In order to avoid sub-optimal performance an enhanced unscented Kalman filter is used. The enhancement is that a combination of different motion models is exploited, leading to a more accurate modeling of the vehicle's movement. For this combination the curvature of the road from a digital map and the yaw rate and acceleration values from the vehicle are taken into consideration. The performance of the proposed unscented filter is proven to be superior compared to the performance of both the extended Kalman filter and the simple unscented filter which takes into account only a specific motion model.

The use of multiple hypothesis tracking has proven to provide significant performance benefits over the single hypothesis GNN or the PDA algorithm. Automotive sensors like radars, laser-scanners or vision systems are being integrated into vehicles for commercial or scientific purposes, in increasing numbers over the last years. As a result, there is profound literature on this area and several approaches have been proposed to the problem of multi-target, multi-sensor target tracking. The most advanced vehicle applications allow the use of highly or even fully automated driving. Of course, these applications require an accurate, robust and reliable perception output so that the vehicle can be driven autonomously. The HAVEit EU project investigates the application and validation of automated vehicles applications, technologies that are going to have great impact in transport safety and comfort. In this paper the MHT algorithm is applied to real sensor data, installed in Volvo Technology vehicle demonstrating Automated Queue Assistance. In conjunction with simulated scenarios, the benefits in tracking performance compared to conventional GNN tracking are presented.

The Adaptive Integrated Driver-vehicle interfacE (AIDE) is an integrated project funded by the European Commission in the Sixth Framework Programme. The project, which involves 31 partners from the European automotive industry and academia, deals with behavioral and technical issues related to automotive human-machine interface (HMI) design, with a particular focus on integration and adaptation. The project involves tightly integrated empirical research, driver-behavior modeling, and methodological and technological development. This paper provides an overview of the AIDE Sub-Project 3 results dealing with the design, development, and integration of the AIDE system in three prototype vehicles, together with the evaluation results of the trials.

This paper deals with the integration of multiple advanced driver-assistance systems (ADAS) and in-vehicle information systems (IVIS) in a holistic driver-support system. The paper presents the results of a project named Integrated Safety Systems (INSAFES), which was part of PReVENT: an integrating project carried out under the European Framework Programme 6. Integration in INSAFES is tackled at three different levels in the framework of a “cognitive car” perspective: 1) at the perception level, to represent the world around the vehicle, including object-tracking between sensor fields and the detection of driver intentions; 2) at the decision level, to reproduce humanlike holistic motion plans, which serve as “reference maneuvers” to evaluate the motion alternatives that a driver faces; and 3) at the level of interaction with the driver and vehicle control ( action level), to arbitrate between the requests of functions competing for driver attention. A function that provides simultaneous longitudinal and lateral support has been developed. It gives support for safe speed, safe distance, lane change, and all-around collision avoidance all at the same time. At its core, there is a tool (evasive/reference maneuver) that constantly evaluates two possible alternatives (in lane and evasive/lane change) and compares them with the driver input to detect which one applies, which dictates warnings and driver interactions, and whether there is a better alternative. In addition, a “warning manager” has been developed, acting like a referee who lets the ADAS applications work standalone and then combines the requests of each application, prioritizes them, and manages the interaction with the user. The warning manager can be particularly useful in the case of integration of pre-existing standalone functions, which can be quickly reused. If a holistic ADAS is developed, the warning manager can still be used to combine it with IVIS functions. In fact, depending on the kind of ADAS and IVIS considered, the most suitable approach can be either to combine functions in a unified multifunctional driver-support application or to arbitrate between them through the warning manager.

Going beyond standard lane-departure-avoidance systems, this paper addresses the development of a system that is able to deal with a large set of different traffic situations. Its foundation lies on a thoroughly constituted environment detection through which a decision system is built. From the output of the decision module, the driver is warned or corrected through suited actuators that are coupled to control strategies. The input to the system comes from cameras, which are supplemented by active sensors (such as radar and laser scanners) and vehicle dynamic data, digital road maps, and precise vehicle-positioning data. In this paper, the presented system design is divided into three layers: the perception layer, which is responsible for the environment perception, and the decision and action layers, which are responsible for evaluating and executing actions, respectively.

The eight papers in this special section present some of the latest research developments and breakthroughs achieved in the area of active safety as a result of cooperation between key industrial and academic players all over Europe.

This paper presents simulation results demonstrating the performance of OFDM system over vehicular ad-hoc networks, based on IEEE 802.11p specifications, employing turbo coding. The system is focused on vehicle safety applications using ad-hoc communication and networking. IEEE 802.11p defines modifications to IEEE 802.11 to enhance road transport and traffic applications. The standard is based on IEEE 802.11a and intends to support both public safety and licensed private operations over communication channels. The current contribution presents results of system performance evaluation of IEEE 802.11p turbo coding scheme and compares it to the already specified scheme. The turbo coding scheme achieves significant improvement to the performance compared to other techniques. The FEC system model with and without the turbo scheme is later implemented in a Field Programmable Gate Array (FPGA) from Xilinx using an Electronic System Level (ESL) methodology.

Multi-sensor systems in automotive safety applications and sensor data fusion have become very popular in recent years. Sensors on board cars and active safety applications are increasing in number and the need to define a common method for object extraction and serving these applications has been recognized. Authors propose a high level fusion approach suitable for automotive sensor networks with complementary or/and redundant field of views. The advantage of this approach is that it ensures system modularity and allows benchmarking, as it does not permit feedbacks and loops inside the processing. In this paper this track level data fusion approach is introduced with the main focus to be on the data association of tracks coming from the on board sensors with distributed processing. The core of the proposed approach is the formulation of the data association problem in presence of multipoint objects and then the solution for (a) multidimensional assignment and (b) all around vehicle object maintenance. The motivation of this work is the research work that is carried out in the project PReVENT/ProFusion2 where the proposed algorithm is being tested in two experimental vehicles.

In safety automotive applications the system must me capable of early recognizing the maneuvers performed by the driver and the intention associated with them in order to take preventive measures or trigger warning alarms. This is done by the situation refinement level in the fusion system that processes the data provided by the on-board sensors. By recognizing relationships between entities of the road environment the system can react with more efficiency to the current situation. For example the intention of a lane change, the detection of an overtaking maneuver, the estimation of the lane in which the detected vehicle is located, help the system to decide which action must be taken in order to prevent an unwanted situation. This paper focuses on the investigation of methods regarding the identification of the maneuvering type and the intention associated.

Path prediction is the only way that an active safety system can predict a driver's intention. In this paper, a model-based description of the traffic environment is presented - both vehicles and infrastructure - in order to provide, in real time, sufficient information for an accurate prediction of the ego-vehicle's path. The proposed approach is a hierarchical-structured algorithm that fuses traffic environment data with car dynamics in order to accurately predict the trajectory of the ego-vehicle, allowing the active safety system to inform, warn the driver, or intervene when critical situations occur. The algorithms are tested with real data, under normal conditions, for collision warning (CW) and vision-enhancement applications. The results clearly show that this approach allows a dynamic situation and threat assessment and can enhance the capabilities of adaptive cruise control and CW functions by reducing the false alarm rate.

This paper focuses on the solution of the problem of (onboard moving vehicles) multiple sensor data fusion systems. The proposed application uses distributed architectures that operate with sensors or sensor systems and give redundant or complementary information for moving objects. This architecture ensures a modular approach allowing exchangeability and benchmarking using the output of individual trackers, whereas the fusion algorithm gives a solution to the track management problem and the coverage of wide perception areas. The test case is a multi-sensor configuration, which monitors the rear and lateral areas of traffic. Results from simulations and real data show that the given approach allows maintenance of the ID of objects and recognition of the vehicle environment with acceptable rates of false alarm and misses

The question raised in this paper, for the first time, is how the JDL model can be applied in multi-sensor automotive safety systems, since new sensors are integrated on-board, while new functions support the driver, intervene and control the vehicle. The paper proposes a hybrid hierarchical structure and develops a suitable functional model, namely the ProFusion2 (PF2) model; PF2 serves the broad automotive sensor data fusion community as a conceptual framework of common understanding and it provides recommendations and guidelines for implementation of fusion systems. Reference implementations are given as complete examples from the major automotive research initiative in Europe (PReVENTproject)