223 research outputs found

    An overview of VANET vehicular networks

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    Today, with the development of intercity and metropolitan roadways and with various cars moving in various directions, there is a greater need than ever for a network to coordinate commutes. Nowadays, people spend a lot of time in their vehicles. Smart automobiles have developed to make that time safer, more effective, more fun, pollution-free, and affordable. However, maintaining the optimum use of resources and addressing rising needs continues to be a challenge given the popularity of vehicle users and the growing diversity of requests for various services. As a result, VANET will require modernized working practices in the future. Modern intelligent transportation management and driver assistance systems are created using cutting-edge communication technology. Vehicular Ad-hoc networks promise to increase transportation effectiveness, accident prevention, and pedestrian comfort by allowing automobiles and road infrastructure to communicate entertainment and traffic information. By constructing thorough frameworks, workflow patterns, and update procedures, including block-chain, artificial intelligence, and SDN (Software Defined Networking), this paper addresses VANET-related technologies, future advances, and related challenges. An overview of the VANET upgrade solution is given in this document in order to handle potential future problems

    5G for Vehicular Use Cases: Analysis of Technical Requirements, Value Propositions and Outlook

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    The fifth generation (5G) of wireless networks promises to meet the stringent requirements of vehicular use cases that cannot be supported by previous technologies. However, the stakeholders of the automotive industry (e.g., car manufacturers and road operators) are still skeptical about the capability of the telecom industry to take the lead in a market that has been dominated by dedicated intelligent transport systems (ITS) deployments. In this context, this paper constructs a framework where the potential of 5G to support different vehicular use cases is thoroughly examined under a common format from both the technical and business perspectives. From the technical standpoint, a storyboard description is developed to explain when and how different use case scenarios may come into play (i.e., pre-conditions, service flows and post-conditions). Then, a methodology to trial each scenario is developed including a functional architecture, an analysis of the technical requirements and a set of target test cases. From the business viewpoint, an initial analysis of the qualitative value perspectives is conducted considering the stakeholders, identifying the pain points of the existing solutions, and highlighting the added value of 5G in overcoming them. The future evolution of the considered use cases is finally discussed

    A comprehensive survey of V2X cybersecurity mechanisms and future research paths

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    Recent advancements in vehicle-to-everything (V2X) communication have notably improved existing transport systems by enabling increased connectivity and driving autonomy levels. The remarkable benefits of V2X connectivity come inadvertently with challenges which involve security vulnerabilities and breaches. Addressing security concerns is essential for seamless and safe operation of mission-critical V2X use cases. This paper surveys current literature on V2X security and provides a systematic and comprehensive review of the most relevant security enhancements to date. An in-depth classification of V2X attacks is first performed according to key security and privacy requirements. Our methodology resumes with a taxonomy of security mechanisms based on their proactive/reactive defensive approach, which helps identify strengths and limitations of state-of-the-art countermeasures for V2X attacks. In addition, this paper delves into the potential of emerging security approaches leveraging artificial intelligence tools to meet security objectives. Promising data-driven solutions tailored to tackle security, privacy and trust issues are thoroughly discussed along with new threat vectors introduced inevitably by these enablers. The lessons learned from the detailed review of existing works are also compiled and highlighted. We conclude this survey with a structured synthesis of open challenges and future research directions to foster contributions in this prominent field.This work is supported by the H2020-INSPIRE-5Gplus project (under Grant agreement No. 871808), the โ€Ministerio de Asuntos Econรณmicos y Transformacion Digitalโ€ and the European Union-NextGenerationEU in the frameworks of the โ€Plan de Recuperaciรณn, Transformaciรณn y Resilienciaโ€ and of the โ€Mecanismo de Recuperaciรณn y Resilienciaโ€ under references TSI-063000-2021-39/40/41, and the CHIST-ERA-17-BDSI-003 FIREMAN project funded by the Spanish National Foundation (Grant PCI2019-103780).Peer ReviewedPostprint (published version

    A systematic literature review on the relationship between autonomous vehicle technology and traffic-related mortality.

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ํ–‰์ •๋Œ€ํ•™์› ๊ธ€๋กœ๋ฒŒํ–‰์ •์ „๊ณต, 2023. 2. ์ตœํƒœํ˜„.The society is anticipated to gain a lot from Autonomous Vehicles (AV), such as improved traffic flow and a decrease in accidents. They heavily rely on improvements in various Artificial Intelligence (AI) processes and strategies. Though some researchers in this field believe AV is the key to enhancing safety, others believe AV creates new challenges when it comes to ensuring the security of these new technology/systems and applications. The article conducts a systematic literature review on the relationship between autonomous vehicle technology and traffic-related mortality. According to inclusion and exclusion criteria, articles from EBSCO, ProQuest, IEEE Explorer, Web of Science were chosen, and they were then sorted. The findings reveal that the most of these publications have been published in advanced transport-related journals. Future improvements in the automobile industry and the development of intelligent transportation systems could help reduce the number of fatal traffic accidents. Technologies for autonomous cars provide effective ways to enhance the driving experience and reduce the number of traffic accidents. A multitude of driving-related problems, such as crashes, traffic, energy usage, and environmental pollution, will be helped by autonomous driving technology. More research is needed for the significant majority of the studies that were assessed. They need to be expanded so that they can be tested in real-world or computer-simulated scenarios, in better and more realistic scenarios, with better and more data, and in experimental designs where the results of the proposed strategy are compared to those of industry standards and competing strategies. Therefore, additional study with improved methods is needed. Another major area that requires additional research is the moral and ethical choices made by AVs. Government, policy makers, manufacturers, and designers all need to do many actions in order to deploy autonomous vehicles on the road effectively. The government should develop laws, rules, and an action plan in particular. It is important to create more effective programs that might encourage the adoption of emerging technology in transportation systems, such as driverless vehicles. In this regard, user perception becomes essential since it may inform designers about current issues and observations made by people. The perceptions of autonomous car users in developing countries like Azerbaijan haven't been thoroughly studied up to this point. The manufacturer has to fix the system flaw and needs a good data set for efficient operation. In the not-too-distant future, the widespread use of highly automated vehicles (AVs) may open up intriguing new possibilities for resolving persistent issues in current safety-related research. Further research is required to better understand and quantify the significant policy implications of Avs, taking into consideration factors like penetration rate, public adoption, technological advancements, traffic patterns, and business models. It only needs to take into account peer-reviewed, full-text journal papers for the investigation, but it's clear that a larger database and more documents would provide more results and a more thorough analysis.์ž์œจ์ฃผํ–‰์ฐจ(AV)๋ฅผ ํ†ตํ•ด ๊ตํ†ต ํ๋ฆ„์ด ๊ฐœ์„ ๋˜๊ณ  ์‚ฌ๊ณ ๊ฐ€ ์ค„์–ด๋“œ๋Š” ๋“ฑ ์‚ฌํšŒ๊ฐ€ ์–ป๋Š” ๊ฒƒ์ด ๋งŽ์„ ๊ฒƒ์œผ๋กœ ์˜ˆ์ƒ๋œ๋‹ค. ๊ทธ๋“ค์€ ๋‹ค์–‘ํ•œ ์ธ๊ณต์ง€๋Šฅ(AI) ํ”„๋กœ์„ธ์Šค์™€ ์ „๋žต์˜ ๊ฐœ์„ ์— ํฌ๊ฒŒ ์˜์กดํ•œ๋‹ค. ์ด ๋ถ„์•ผ์˜ ์ผ๋ถ€ ์—ฐ๊ตฌ์ž๋“ค์€ AV๊ฐ€ ์•ˆ์ „์„ฑ์„ ํ–ฅ์ƒ์‹œํ‚ค๋Š” ์—ด์‡ ๋ผ๊ณ  ๋ฏฟ์ง€๋งŒ, ๋‹ค๋ฅธ ์—ฐ๊ตฌ์ž๋“ค์€ AV๊ฐ€ ์ด๋Ÿฌํ•œ ์ƒˆ๋กœ์šด ๊ธฐ์ˆ /์‹œ์Šคํ…œ ๋ฐ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜์˜ ๋ณด์•ˆ์„ ๋ณด์žฅํ•˜๋Š” ๊ฒƒ๊ณผ ๊ด€๋ จํ•˜์—ฌ ์ƒˆ๋กœ์šด ๋ฌธ์ œ๋ฅผ ์•ผ๊ธฐํ•œ๋‹ค๊ณ  ๋ฏฟ๋Š”๋‹ค. ์ด ๋…ผ๋ฌธ์€ ์ž์œจ์ฃผํ–‰์ฐจ ๊ธฐ์ˆ ๊ณผ ๊ตํ†ต ๊ด€๋ จ ์‚ฌ๋ง๋ฅ  ์‚ฌ์ด์˜ ๊ด€๊ณ„์— ๋Œ€ํ•œ ์ฒด๊ณ„์ ์ธ ๋ฌธํ—Œ ๊ฒ€ํ† ๋ฅผ ์ˆ˜ํ–‰ํ•œ๋‹ค. ํฌํ•จ ๋ฐ ์ œ์™ธ ๊ธฐ์ค€์— ๋”ฐ๋ผ EBSCO, ProQuest, IEEE Explorer ๋ฐ Web of Science์˜ ๊ธฐ์‚ฌ๋ฅผ ์„ ํƒํ•˜๊ณ  ๋ถ„๋ฅ˜ํ–ˆ๋‹ค.์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” ์ด๋Ÿฌํ•œ ์ถœํŒ๋ฌผ์˜ ๋Œ€๋ถ€๋ถ„์ด ๊ณ ๊ธ‰ ์šด์†ก ๊ด€๋ จ ์ €๋„์— ๊ฒŒ์žฌ๋˜์—ˆ์Œ์„ ๋ณด์—ฌ์ค€๋‹ค. ๋ฏธ๋ž˜์˜ ์ž๋™์ฐจ ์‚ฐ์—…์˜ ๊ฐœ์„ ๊ณผ ์ง€๋Šฅํ˜• ๊ตํ†ต ์‹œ์Šคํ…œ์˜ ๊ฐœ๋ฐœ์€ ์น˜๋ช…์ ์ธ ๊ตํ†ต ์‚ฌ๊ณ ์˜ ์ˆ˜๋ฅผ ์ค„์ด๋Š” ๋ฐ ๋„์›€์ด ๋  ์ˆ˜ ์žˆ๋‹ค. ์ž์œจ์ฃผํ–‰ ์ž๋™์ฐจ ๊ธฐ์ˆ ์€ ์šด์ „ ๊ฒฝํ—˜์„ ํ–ฅ์ƒ์‹œํ‚ค๊ณ  ๊ตํ†ต ์‚ฌ๊ณ ์˜ ์ˆ˜๋ฅผ ์ค„์ผ ์ˆ˜ ์žˆ๋Š” ํšจ๊ณผ์ ์ธ ๋ฐฉ๋ฒ•์„ ์ œ๊ณตํ•œ๋‹ค. ์ถฉ๋Œ, ๊ตํ†ต, ์—๋„ˆ์ง€ ์‚ฌ์šฉ, ํ™˜๊ฒฝ ์˜ค์—ผ๊ณผ ๊ฐ™์€ ์ˆ˜๋งŽ์€ ์šด์ „ ๊ด€๋ จ ๋ฌธ์ œ๋“ค์€ ์ž์œจ ์ฃผํ–‰ ๊ธฐ์ˆ ์— ์˜ํ•ด ๋„์›€์„ ๋ฐ›์„ ๊ฒƒ์ด๋‹ค. ํ‰๊ฐ€๋œ ๋Œ€๋ถ€๋ถ„์˜ ์—ฐ๊ตฌ์— ๋Œ€ํ•ด ๋” ๋งŽ์€ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ์‹ค์ œ ๋˜๋Š” ์ปดํ“จํ„ฐ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ์‹œ๋‚˜๋ฆฌ์˜ค, ๋” ์ข‹๊ณ  ํ˜„์‹ค์ ์ธ ์‹œ๋‚˜๋ฆฌ์˜ค, ๋” ์ข‹๊ณ  ๋” ๋งŽ์€ ๋ฐ์ดํ„ฐ, ๊ทธ๋ฆฌ๊ณ  ์ œ์•ˆ๋œ ์ „๋žต ๊ฒฐ๊ณผ๊ฐ€ ์‚ฐ์—… ํ‘œ์ค€ ๋ฐ ๊ฒฝ์Ÿ ์ „๋žต์˜ ๊ฒฐ๊ณผ์™€ ๋น„๊ต๋˜๋Š” ์‹คํ—˜ ์„ค๊ณ„์—์„œ ํ…Œ์ŠคํŠธ๋  ์ˆ˜ ์žˆ๋„๋ก ํ™•์žฅ๋˜์–ด์•ผ ํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ๊ฐœ์„ ๋œ ๋ฐฉ๋ฒ•์— ๋Œ€ํ•œ ์ถ”๊ฐ€ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ์ถ”๊ฐ€ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•œ ๋˜ ๋‹ค๋ฅธ ์ฃผ์š” ๋ถ„์•ผ๋Š” AV์˜ ๋„๋•์ , ์œค๋ฆฌ์  ์„ ํƒ์ด๋‹ค. ์ •๋ถ€, ์ •์ฑ… ์ž…์•ˆ์ž, ์ œ์กฐ์—…์ฒด ๋ฐ ์„ค๊ณ„์ž๋Š” ๋ชจ๋‘ ์ž์œจ ์ฃผํ–‰ ์ฐจ๋Ÿ‰์„ ํšจ๊ณผ์ ์œผ๋กœ ๋„๋กœ์— ๋ฐฐ์น˜ํ•˜๊ธฐ ์œ„ํ•ด ๋งŽ์€ ์กฐ์น˜๋ฅผ ์ทจํ•ด์•ผ ํ•œ๋‹ค. ์ •๋ถ€๋Š” ํŠนํžˆ ๋ฒ•, ๊ทœ์น™, ์‹คํ–‰ ๊ณ„ํš์„ ๊ฐœ๋ฐœํ•ด์•ผ ํ•œ๋‹ค. ์šด์ „์ž ์—†๋Š” ์ฐจ๋Ÿ‰๊ณผ ๊ฐ™์€ ์šด์†ก ์‹œ์Šคํ…œ์—์„œ ์ƒˆ๋กœ์šด ๊ธฐ์ˆ ์˜ ์ฑ„ํƒ์„ ์žฅ๋ คํ•  ์ˆ˜ ์žˆ๋Š” ๋ณด๋‹ค ํšจ๊ณผ์ ์ธ ํ”„๋กœ๊ทธ๋žจ์„ ๋งŒ๋“œ๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•˜๋‹ค. ์ด์™€ ๊ด€๋ จํ•˜์—ฌ, ์„ค๊ณ„์ž์—๊ฒŒ ํ˜„์žฌ ์ด์Šˆ์™€ ์‚ฌ๋žŒ์— ์˜ํ•œ ๊ด€์ฐฐ์„ ์•Œ๋ ค์ค„ ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ์‚ฌ์šฉ์ž ์ธ์‹์ด ํ•„์ˆ˜์ ์ด ๋œ๋‹ค.์ œ์กฐ์—…์ฒด๋Š” ์‹œ์Šคํ…œ ๊ฒฐํ•จ์„ ์ˆ˜์ •ํ•ด์•ผ ํ•˜๋ฉฐ ํšจ์œจ์ ์ธ ์ž‘๋™์„ ์œ„ํ•ด ์ข‹์€ ๋ฐ์ดํ„ฐ ์„ธํŠธ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ๋ฉ€์ง€ ์•Š์€ ๋ฏธ๋ž˜์—, ๊ณ ๋„๋กœ ์ž๋™ํ™”๋œ ์ฐจ๋Ÿ‰(AV)์˜ ๊ด‘๋ฒ”์œ„ํ•œ ์‚ฌ์šฉ์€ ํ˜„์žฌ์˜ ์•ˆ์ „ ๊ด€๋ จ ์—ฐ๊ตฌ์—์„œ ์ง€์†์ ์ธ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•œ ํฅ๋ฏธ๋กœ์šด ์ƒˆ๋กœ์šด ๊ฐ€๋Šฅ์„ฑ์„ ์—ด์–ด์ค„ ์ˆ˜ ์žˆ๋‹ค. ๋ณด๊ธ‰๋ฅ , ๊ณต๊ณต ์ฑ„ํƒ, ๊ธฐ์ˆ  ๋ฐœ์ „, ๊ตํ†ต ํŒจํ„ด ๋ฐ ๋น„์ฆˆ๋‹ˆ์Šค ๋ชจ๋ธ๊ณผ ๊ฐ™์€ ์š”์†Œ๋ฅผ ๊ณ ๋ คํ•˜์—ฌ Avs์˜ ์ค‘์š”ํ•œ ์ •์ฑ… ์˜ํ–ฅ์„ ๋” ์ž˜ ์ดํ•ดํ•˜๊ณ  ์ •๋Ÿ‰ํ™”ํ•˜๊ธฐ ์œ„ํ•œ ์ถ”๊ฐ€ ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ์กฐ์‚ฌ๋ฅผ ์œ„ํ•ด ๋™๋ฃŒ ๊ฒ€ํ† ๋ฅผ ๊ฑฐ์นœ ์ „๋ฌธ ์ €๋„ ๋…ผ๋ฌธ๋งŒ ๊ณ ๋ คํ•˜๋ฉด ๋˜์ง€๋งŒ, ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค๊ฐ€ ์ปค์ง€๊ณ  ๋ฌธ์„œ๊ฐ€ ๋งŽ์•„์ง€๋ฉด ๋” ๋งŽ์€ ๊ฒฐ๊ณผ์™€ ๋” ์ฒ ์ €ํ•œ ๋ถ„์„์ด ์ œ๊ณต๋  ๊ฒƒ์ด ๋ถ„๋ช…ํ•˜๋‹ค.Abstract 3 Table of Contents 6 List of Tables 7 List of Figures 7 List of Appendix 7 CHAPTER 1: INTRODUCTION 8 1.1. Background 8 1.2. Purpose of Research 13 CHAPTER 2: AUTONOMOUS VEHICLES 21 2.1. Intelligent Traffic Systems 21 2.2. System Architecture for Autonomous Vehicles 22 2.3. Key components in AV classification 27 CHAPTER 3: METHODOLOGY AND DATA COLLECTION PROCEDURE 35 CHAPTER 4: FINDINGS AND DISCUSSION 39 4.1. RQ1: Do autonomous vehicles reduce traffic-related deaths 40 4.2. RQ2: Are there any challenges to using autonomous vehicles 63 4.3. RQ3: As a developing country, how effective is the use of autonomous vehicles for reducing traffic mortality 72 CHAPTER 5: CONCLUSION 76 5.1. Summary 76 5.2. Implications and Recommendations 80 5.3. Limitation of the study 91 Bibliography 93 List of Tables Table 1: The 6 Levels of Autonomous Vehicles Table 2: Search strings Table 3: Inclusion and exclusion criteria List of Figures Figure 1: Traffic Death Comparison with Europe Figure 2: Research strategy and study selection process List of Appendix Appendix 1: List of selected articles์„

    An Emulation Framework for Evaluating V2X Communications in C-ITS Applications

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    C-ITS enhances transportation systems with advanced communication tech, enabling vehicle-to-vehicle and vehicle-to-infrastructure data exchange for real-time decision-making. The thesis explores C-ITS concepts, DSRC, and C-V2X tech, and proposes a versatile C-ITS framework for app prototyping and communication evaluation. Real-world tests and simulations validate its potential to improve road safety and efficiency, suggesting integration opportunities for stakeholders and promoting a smarter, sustainable transportation ecosystem

    Future cities and autonomous vehicles: analysis of the barriers to full adoption

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    The inevitable upcoming technology of autonomous vehicles (AVs) will affect our cities and several aspects of our lives. The widespread adoption of AVs repose at crossing distinct barriers that prevent their full adoption. This paper presents a critical review of recent debates about AVs and analyse the key barriers to their full adoption. This study has employed a mixed research methodology on a selected database of recently published research works. Thus, the outcomes of this review integrate the barriers into two main categories; (1) User/Government perspectives that include (i) Users' acceptance and behaviour, (ii) Safety, and (iii) Legislation. (2) Information and Communication Technologies (ICT) which include (i) Computer software and hardware, (ii) Communication systems V2X, and (iii) accurate positioning and mapping. Furthermore, a framework of barriers and their relations to AVs system architecture has been suggested to support future research and technology development

    Automotive applications of high precision GNSS

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    This thesis aims to show that Global Navigation Satellite Systems (GNSS) positioning can play a significant role in the positioning systems of future automotive applications. This is through the adoption of state-of-the-art GNSS positioning technology and techniques, and the exploitation of the rapidly developing vehicle-to-vehicle concept. The merging together of these two developments creates greater performance than can be achieved separately. The original contribution of this thesis comes from this combination: Through the introduction of the Pseudo-VRS concept. Pseudo-VRS uses the princples of Network Real Time Kinematic (N-RTK) positioning to share GNSS information between vehicles, which enables absolute vehicle positioning. Pseudo-VRS is shown to improve the performance of high precision GNSS positioning for road vehicles, through the increased availability of GNSS correction messages and the rapid resolution of the N-RTK fixed solution. Positioning systems in the automotive sector are dominated by satellite-based solutions provided by GNSS. This has been the case since May 2001, when the United States Department of Defense switched off Selective Availability, enabling significantly improved positioning performance for civilian users. The average person most frequently encounters GNSS when using electronic personal navigation devices. The Sat Nav or GPS Navigator is ubiquitous in modern societies, where versions can be found on nomadic devices such as smartphones and dedicated personal navigation devices, or built in to the dashboards of vehicles. Such devices have been hugely successful due to their intrinsic ability to provide position information anywhere in the world with an accuracy of approximately 10 metres, which has proved ideal for general navigation applications. There are a few well known limitations of GNSS positioning, including anecdotal evidence of incorrect navigation advice for personal navigation devices, but these are minor compared to the overall positioning performance. Through steady development of GNSS positioning devices, including the integration of other low cost sensors (for instance, wheel speed or odometer sensors in vehicles), and the development of robust map matching algorithms, the performance of these devices for navigation applications is truly incredible. However, when tested for advanced automotive applications, the performance of GNSS positioning devices is found to be inadequate. In particular, in the most advanced fields of research such as autonomous vehicle technology, GNSS positioning devices are relegated to a secondary role, or often not used at all. They are replaced by terrestrial sensors that provide greater situational awareness, such as radar and lidar. This is due to the high performance demand of such applications, including high positioning accuracy (sub-decimetre), high availability and continuity of solutions (100%), and high integrity of the position information. Low-cost GNSS receivers generally do not meet such requirements. This could be considered an enormous oversight, as modern GNSS positioning technology and techniques have significantly improved satellite-based positioning performance. Other non-GNSS techniques also have their limitations that GNSS devices can minimise or eliminate. For instance, systems that rely on situational awareness require accurate digital maps of their surroundings as a reference. GNSS positioning can help to gather this data, provide an input, and act as a fail-safe in the event of digital map errors. It is apparent that in order to deliver advanced automotive applications - such as semi- or fully-autonomous vehicles - there must be an element of absolute positioning capability. Positioning systems will work alongside situational awareness systems to enable the autonomous vehicles to navigate through the real world. A strong candidate for the positioning system is GNSS positioning. This thesis builds on work already started by researchers at the University of Nottingham, to show that N-RTK positioning is one such technique. N-RTK can provide sub-decimetre accuracy absolute positioning solutions, with high availability, continuity, and integrity. A key component of N-RTK is the availability of real-time GNSS correction data. This is typically delivered to the GNSS receiver via mobile internet (for a roving receiver). This can be a significant limitation, as it relies on the performance of the mobile communications network, which can suffer from performance degradation during dynamic operation. Mobile communications systems are expected to improve significantly over the next few years, as consumers demand faster download speeds and wider availability. Mobile communications coverage already covers a high percentage of the population, but this does not translate into a high percentage of a country's geography. Pockets of poor coverage, often referred to as notspots, are widespread. Many of these notspots include the transportation infrastructure. The vehicle-to-vehicle concept has made significant forward steps in the last few years. Traditionally promoted as a key component of future automotive safety applications, it is now driven primarily by increased demand for in-vehicle infotainment. The concept, which shares similarities with the Internet of Things and Mobile Ad-hoc Networks, relies on communication between road vehicles and other road agents (such as pedestrians and road infrastructure). N-RTK positioning can take advantage of this communication link to minimise its own communications-related limitations. Sharing GNSS information between local GNSS receivers enables better performance of GNSS positioning, based on the principles of differential GNSS and N-RTK positioning techniques. This advanced concept is introduced and tested in this thesis. The Pseudo VRS concept follows the protocols and format of sharing GNSS data used in N-RTK positioning. The technique utilises the latest GNSS receiver design, including multiple frequency measurements and high quality antennas

    Detection, control and mitigation system for secure vehicular communication

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    The increase in the safety and privacy of automated vehicle drivers against hazardous cyber-attacks will lead to a considerable reduction in the number of global deaths and injuries. In this sense, the European Commission has focused attention on the security of communications in high-risk systems when receiving a cyber-attack such as automated vehicles. The project SerIoT comes up as an possible solution, providing a useful open and reference framework for real-time monitoring of the traffic exchanged through heterogeneous IoT platforms. This system is capable of recognize suspicious patterns, evaluate them and finally take mitigate actions. The paper presents a use case of the SerIoT project related to rerouting tests in vehicular communication. The goal is to ensure secure and reliable communication among Connected Intelligent Transportation Systems (C-ITS) components (vehicles, infrastructures, etc) using the SerIoT's system capabilities to detect and mitigate possible network attacks. Therefore, fleet management and smart intersection scenarios were chosen, where vehicles equipped with On Board Units (OBU) interact with each other and Road Side Units (RSU) to accomplish an optimal flow of traffic. These equipments use the SerIoT systems to deal with cyber-attacks such as Denial of Service (DoS). Tests have been validated in different scenarios under threats situations. It shows the great performance of the SerIoT system taking the corresponding actions to ensure a continuous and safety traffic flow
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