Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

INTELLIGENTE TRANSPORT SYSTEMEN ITS EN VERKEERSVEILIGHEID

This report discusses Intelligent Transport Systems (ITS). This generic term is used for a broad range of information-, control- and electronic technology that can be integrated in the road infrastructure and the vehicles themselves, saving lives, time and money bymonitoring and managing traffic flows, reducing conges-tion, avoiding accidents, etc. Because this report was written in the scope of the Policy Research Centre Mobility & Public Works, track Traffic Safety, it focuses on ITS systems from the traffic safety point of view. Within the whole range of ITS systems, two categories can be distinguished: autonomous and cooperative systems. Autonomous systems are all forms of ITS which operate by itself, and do not depend on the cooperation with other vehicles or supporting infrastructure. Example applications are blind spot detection using radar, electronic stability control, dynamic traffic management using variable road signs, emergency call, etc. Cooperative systems are ITS systems based on communication and cooperation, both between vehicles as between vehicles and infrastructure. Example applications are alerting vehicles approaching a traffic jam, exchanging data regarding hazardous road conditions, extended electronic brake light, etc. In some cases, autonomous systems can evolve to autonomous cooperative systems. ISA (Intelligent Speed Adaptation) is an example of this: the dynamic aspect as well as communication with infrastructure (eg Traffic lights, Variable Message Sign (VMS)...) can provide additional road safety. This is the clear link between the two parts of this report. The many ITS applications are an indicator of the high expectations from the government, the academic world and the industry regarding the possibilities made possible by both categories of ITS systems. Therefore, the comprehensive discussion of both of them is the core of this report. The first part of the report covering the autonomous systems treats two aspects: 1. Overview of European projects related to mobility and in particular to road safety 2. Overview for guidelines for the evaluation of ITS projects. Out of the wide range of diverse (autonomous) ITS applications a selection is made; this selection is focused on E Safety Forum and PreVENT. Especially the PreVent research project is interesting because ITS-applications have led to a number of concrete demonstration vehicles that showed - in protected and unprotected surroundings- that these ITS-applications are already technically useful or could be developed into useful products. The component “guidelines for the evaluation of ITS projects” outlines that the government has to have specific evaluation tools if the government has the ambition of using ITS-applications for road safety. Two projects -guidelines for the evaluation of ITS projects- are examined; a third evaluation method is only mentioned because this description shows that a specific targeting of the government can be desirable : 1. TRACE describes the guidelines for the evaluation of ITS projects which are useful for the evaluation of specific ITS-applications. 2. FITS contains Finnish guidelines for the evaluation of ITS project; FIS is an adaptation of methods used for evaluation of transport projects. 3. The third evaluation method for the evaluation of ITS projects is developed in an ongoing European research project, eImpact. eImpact is important because, a specific consultation of stake holders shows that the social importance of some techniques is underestimated. These preliminary results show that an appropriate guiding role for the government could be important. In the second part of this document the cooperative systems are discussed in depth. These systems enable a large number of applications with an important social relevance, both on the level of the environment, mobility and traffic safety. Cooperative systems make it possible to warn drivers in time to avoid collisions (e.g. when approaching the tail of a traffic jam, or when a ghost driver is detected). Hazardous road conditions can be automatically communicated to other drivers (e.g. after the detection of black ice or an oil trail by the ESP). Navigation systems can receive detailed real-time up-dates about the current traffic situation and can take this into account when calculating their routes. When a traffic distortion occurs, traffic centers can immediately take action and can actively influence the way that the traffic will be diverted. Drivers can be notified well in advance about approaching emergency vehicles, and can be directed to yield way in a uniform manner. This is just a small selection from the large number of applications that are made possible because of cooperative ITS systems, but it is very obvious that these systems can make a significant positive contribution to traffic safety. In literature it is estimated that the decrease of accidents with injuries of fatalities will be between 20% and 50% . It is not suprising that ITS systems receive a lot of attention for the moment. On an international level, a number of standards are being established regarding this topic. The International Telecommunications Uniont (ITU), Institute for Electrical and Electronics Engineers (IEEE), International Organization for Standardization (ISO), Association of Radio Industries and Business (ARIB) and European committee for standardization (CEN) are currently defining standards that describe different aspects of ITS systems. One of the names that is mostly mentioned in literature is the ISO TC204/WG16 Communications Architecture for Land Mobile environment (CALM) standard. It describes a framework that enables transparent (both for the application and the user) continuous communication through different communication media. Besides the innumerable standardization activities, there is a great number of active research projects. On European level, the most important are the i2010 Intelligent Car Initiative, the eSafety Forum, and the COMeSafety, the CVIS, the SAFESPOT, the COOPERS and the SEVECOM project. The i2010 Intelligent Car Initiative is an European initiative with the goal to halve the number of traffic casualties by 2010. The eSafety Forum is an initiative of the European Commission, industry and other stakeholders and targets the acceleration of development and deployment of safety-related ITS systems. The COMeSafety project supports the eSafety Forum on the field of vehicle-to-vehicle and vehicle-to-infrastructure communication. In the CVIS project, attention is given to both technical and non-technical issues, with the main goal to develop the first free and open reference implementation of the CALM architecture. The SAFEST project investigates which data is important for safety applications, and with which algorithmsthis data can be extracted from vehicles and infrastructure. The COOPERS project mainly targets communication between vehicles and dedicated roadside infrastructure. Finally, the SEVECOM project researches security and privacy issues. Besides the European projects, research is also conducted in the United States of America (CICAS and VII projects) and in Japan (AHSRA, VICS, Smartway, internetITS). Besides standardization bodies and governmental organizations, also the industry has a considerable interest in ITS systems. In the scope of their ITS activities, a number of companies are united in national and international organizations. On an international level, the best known names are the Car 2 Car Communication Consortium, and Ertico. The C2C CC unites the large European car manufacturers, and focuses on the development of an open standard for vehicle-to-vehicle and vehicle-to-infrastructure communications based on the already well established IEEE 802.11 WLAN standard. Ertico is an European multi-sector, public/private partnership with the intended purpose of the development and introduction of ITS systems. On a national level, FlandersDrive and The Telematics Cluster / ITS Belgium are the best known organizations. Despite the worldwide activities regarding (cooperative) ITS systems, there still is no consensus about the wireless technology to be used in such systems. This can be put down to the fact that a large number of suitable technologies exist or are under development. Each technology has its specific advantages and disadvantages, but no single technology is the ideal solution for every ITS application. However, the different candidates can be classified in three distinct categories. The first group contains solutions for Dedicated Short Range Communication (DSRC), such as the WAVE technology. The second group is made up of several cellular communication networks providing coverage over wide areas. Examples are GPRS (data communication using the GSM network), UMTS (faster then GPRS), WiMAX (even faster then UMTS) and MBWA (similar to WiMAX). The third group consists of digital data broadcast technologies such as RDS (via the current FM radio transmissions, slow), DAB and DMB (via current digital radio transmissions, quicker) and DVB-H (via future digital television transmissions for mobiledevices, quickest). The previous makes it clear that ITS systems are a hot topic right now, and they receive a lot of attention from the academic world, the standardization bodies and the industry. Therefore, it seems like that it is just a matter of time before ITS systems will find their way into the daily live. Due to the large number of suitable technologies for the implementation of cooperative ITS systems, it is very hard to define which role the government has to play in these developments, and which are the next steps to take. These issues were addressed in reports produced by the i2010 Intelligent Car Initiative and the CVIS project. Their state of the art overview revealed that until now, no country has successfully deployed a fully operational ITS system yet. Seven EU countries are the furthest and are already in the deployment phase: Sweden, Germany, the Netherlands, the United Kingdom, Finland, Spain and France. These countries are trailed by eight countries which are in the promotion phase: Denmark, Greece, Italy, Austria, Belgium,Norway, the Czech Republic and Poland. Finally, the last ten countries find themselves in the start-up phase: Estonia, Lithuania, Latvia, Slovenia, Slovakia, Hungary, Portugal, Switzerland, Ireland and Luxembourg. These European reports produced by the i2010 Intelligent Car Initiative and the CVIS project have defined a few policy recommendations which are very relevant for the Belgian and Flemish government. The most important recommendations for the Flemish government are: • Support awareness: research revealed that civilians consider ITS applications useful, but they are not really willing to pay for this technology. Therefore, it is important to convince the general public of the usefulness and the importance of ITS systems. • Fill the gaps: Belgium is situated in the promotion phase. This means that it should focus at identifying the missing stakeholders, and coordinating national and regional ITS activities. Here it is important that the research activities are coordinated in a national and international context to allow transfer of knowledge from one study to the next, as well as the results to be comparable. • Develop a vision: in the scope of ITS systems policies have to be defined regarding a large number of issues. For instance there is the question if ITS users should be educated, meaning that the use of ITS systems should be the subject of the drivers license exam. How will the regulations be for the technical inspection of vehicles equipped with ITS technology? Will ITS systems be deployed on a voluntary base, or will they e.g. be obliged in every new car? Will the services be offered by private companies, by the public authorities, or by a combination of them? Which technology will be used to implement ITS systems? These are just a few of the many questions where the government will have to develop a point of view for. • Policy coordination: ITS systems are a policy subject on an international, national and regional level. It is very important that these policy organizations can collaborate in a coordinated manner. • Iterative approach to policy development: developing policies for this complex matter is not a simple task. This asks for an iterative approach, where policy decisions are continuously refined and adjusted

Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions

[EN] Advances in information and signal processing technologies have a significant impact on autonomous driving (AD), improving driving safety while minimizing the efforts of human drivers with the help of advanced artificial intelligence (AI) techniques. Recently, deep learning (DL) approaches have solved several real-world problems of complex nature. However, their strengths in terms of control processes for AD have not been deeply investigated and highlighted yet. This survey highlights the power of DL architectures in terms of reliability and efficient real-time performance and overviews state-of-the-art strategies for safe AD, with their major achievements and limitations. Furthermore, it covers major embodiments of DL along the AD pipeline including measurement, analysis, and execution, with a focus on road, lane, vehicle, pedestrian, drowsiness detection, collision avoidance, and traffic sign detection through sensing and vision-based DL methods. In addition, we discuss on the performance of several reviewed methods by using different evaluation metrics, with critics on their pros and cons. Finally, this survey highlights the current issues of safe DL-based AD with a prospect of recommendations for future research, rounding up a reference material for newcomers and researchers willing to join this vibrant area of Intelligent Transportation Systems.This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) Grant funded by the Korea Government (MSIT) (2019-0-00136, Development of AI-Convergence Technologies for Smart City Industry Productivity Innovation); The work of Javier Del Ser was supported by the Basque Government through the EMAITEK and ELKARTEK Programs, as well as by the Department of Education of this institution (Consolidated Research Group MATHMODE, IT1294-19); VHCA received support from the Brazilian National Council for Research and Development (CNPq, Grant #304315/2017-6 and #430274/2018-1).Muhammad, K.; Ullah, A.; Lloret, J.; Del Ser, J.; De Albuquerque, VHC. (2021). Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions. IEEE Transactions on Intelligent Transportation Systems. 22(7):4316-4336. https://doi.org/10.1109/TITS.2020.30322274316433622

Кибербезопасность в образовательных сетях

The paper discusses the possible impact of digital space on a human, as well as human-related directions in cyber-security analysis in the education: levels of cyber-security, social engineering role in cyber-security of education, “cognitive vaccination”. “A Human” is considered in general meaning, mainly as a learner. The analysis is provided on the basis of experience of hybrid war in Ukraine that have demonstrated the change of the target of military operations from military personnel and critical infrastructure to a human in general. Young people are the vulnerable group that can be the main goal of cognitive operations in long-term perspective, and they are the weakest link of the System.У статті обговорюється можливий вплив цифрового простору на людину, а також пов'язані з людиною напрямки кібербезпеки в освіті: рівні кібербезпеки, роль соціального інжинірингу в кібербезпеці освіти, «когнітивна вакцинація». «Людина» розглядається в загальному значенні, головним чином як та, що навчається. Аналіз надається на основі досвіду гібридної війни в Україні, яка продемонструвала зміну цілей військових операцій з військовослужбовців та критичної інфраструктури на людину загалом. Молодь - це вразлива група, яка може бути основною метою таких операцій в довгостроковій перспективі, і вони є найслабшою ланкою системи.В документе обсуждается возможное влияние цифрового пространства на человека, а также связанные с ним направления в анализе кибербезопасности в образовании: уровни кибербезопасности, роль социальной инженерии в кибербезопасности образования, «когнитивная вакцинация». «Человек» рассматривается в общем смысле, в основном как ученик. Анализ представлен на основе опыта гибридной войны в Украине, которая продемонстрировала изменение цели военных действий с военного персонала и критической инфраструктуры на человека в целом. Молодые люди являются уязвимой группой, которая может быть главной целью когнитивных операций в долгосрочной перспективе, и они являются самым слабым звеном Систем

Automotive Intelligence Embedded in Electric Connected Autonomous and Shared Vehicles Technology for Sustainable Green Mobility

The automotive sector digitalization accelerates the technology convergence of perception, computing processing, connectivity, propulsion, and data fusion for electric connected autonomous and shared (ECAS) vehicles. This brings cutting-edge computing paradigms with embedded cognitive capabilities into vehicle domains and data infrastructure to provide holistic intrinsic and extrinsic intelligence for new mobility applications. Digital technologies are a significant enabler in achieving the sustainability goals of the green transformation of the mobility and transportation sectors. Innovation occurs predominantly in ECAS vehicles’ architecture, operations, intelligent functions, and automotive digital infrastructure. The traditional ownership model is moving toward multimodal and shared mobility services. The ECAS vehicle’s technology allows for the development of virtual automotive functions that run on shared hardware platforms with data unlocking value, and for introducing new, shared computing-based automotive features. Facilitating vehicle automation, vehicle electrification, vehicle-to-everything (V2X) communication is accomplished by the convergence of artificial intelligence (AI), cellular/wireless connectivity, edge computing, the Internet of things (IoT), the Internet of intelligent things (IoIT), digital twins (DTs), virtual/augmented reality (VR/AR) and distributed ledger technologies (DLTs). Vehicles become more intelligent, connected, functioning as edge micro servers on wheels, powered by sensors/actuators, hardware (HW), software (SW) and smart virtual functions that are integrated into the digital infrastructure. Electrification, automation, connectivity, digitalization, decarbonization, decentralization, and standardization are the main drivers that unlock intelligent vehicles' potential for sustainable green mobility applications. ECAS vehicles act as autonomous agents using swarm intelligence to communicate and exchange information, either directly or indirectly, with each other and the infrastructure, accessing independent services such as energy, high-definition maps, routes, infrastructure information, traffic lights, tolls, parking (micropayments), and finding emergent/intelligent solutions. The article gives an overview of the advances in AI technologies and applications to realize intelligent functions and optimize vehicle performance, control, and decision-making for future ECAS vehicles to support the acceleration of deployment in various mobility scenarios. ECAS vehicles, systems, sub-systems, and components are subjected to stringent regulatory frameworks, which set rigorous requirements for autonomous vehicles. An in-depth assessment of existing standards, regulations, and laws, including a thorough gap analysis, is required. Global guidelines must be provided on how to fulfill the requirements. ECAS vehicle technology trustworthiness, including AI-based HW/SW and algorithms, is necessary for developing ECAS systems across the entire automotive ecosystem. The safety and transparency of AI-based technology and the explainability of the purpose, use, benefits, and limitations of AI systems are critical for fulfilling trustworthiness requirements. The article presents ECAS vehicles’ evolution toward domain controller, zonal vehicle, and federated vehicle/edge/cloud-centric based on distributed intelligence in the vehicle and infrastructure level architectures and the role of AI techniques and methods to implement the different autonomous driving and optimization functions for sustainable green mobility.publishedVersio