1,246 research outputs found

    Hierarchical multiagent reinforcement learning for maritime traffic management

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    Agency for Science, Technology and Research, Fujitsu Limited; National Research Foundation Singapor

    Multiagent decision making and learning in urban environments

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    Agents and Robots for Reliable Engineered Autonomy

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    This book contains the contributions of the Special Issue entitled "Agents and Robots for Reliable Engineered Autonomy". The Special Issue was based on the successful first edition of the "Workshop on Agents and Robots for reliable Engineered Autonomy" (AREA 2020), co-located with the 24th European Conference on Artificial Intelligence (ECAI 2020). The aim was to bring together researchers from autonomous agents, as well as software engineering and robotics communities, as combining knowledge from these three research areas may lead to innovative approaches that solve complex problems related to the verification and validation of autonomous robotic systems

    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„

    Open Platforms for Connected Vehicles

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

    The collaborative iterative search approach to multi agent path finding

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    PhD ThesisThis thesis presents a new approach to obtaining optimal and complete solutions to Multi Agent Path Finding (MAPF) problems called Collaborative Iterative Search (CIS). CIS employs a conflict based scheme inspired by the Conflict Based Search (CBS) algorithm and extends this to include a linear order lower level search. The structure of Planar Graphs is leveraged, permitting further optimization of the algorithm. This takes the form of reasoning-based culling of the search space, while maintaining optimality and completeness. Benchmarks provided demonstrate significant performance gains over the existing state of the art, particularly in the case of sparsely populated maps. The thesis draws to a conclusion with a summary of proposed future work

    Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior

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    Abstractโ€”Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high-streets. Unlike static obstacles, pedestrians are active agents with complex, inter- active motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behaviour as well as detecting and tracking them. This narrative review article is Part II of a pair, together surveying the current technology stack involved in this process, organising recent research into a hierarchical taxonomy ranging from low-level image detection to high-level psychological models, from the perspective of an AV designer. This self-contained Part II covers the higher levels of this stack, consisting of models of pedestrian behaviour, from prediction of individual pedestriansโ€™ likely destinations and paths, to game-theoretic models of interactions between pedestrians and autonomous vehicles. This survey clearly shows that, although there are good models for optimal walking behaviour, high-level psychological and social modelling of pedestrian behaviour still remains an open research question that requires many conceptual issues to be clarified. Early work has been done on descriptive and qualitative models of behaviour, but much work is still needed to translate them into quantitative algorithms for practical AV control

    Mobile Ad-Hoc Networks

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    Being infrastructure-less and without central administration control, wireless ad-hoc networking is playing a more and more important role in extending the coverage of traditional wireless infrastructure (cellular networks, wireless LAN, etc). This book includes state-of the-art techniques and solutions for wireless ad-hoc networks. It focuses on the following topics in ad-hoc networks: vehicular ad-hoc networks, security and caching, TCP in ad-hoc networks and emerging applications. It is targeted to provide network engineers and researchers with design guidelines for large scale wireless ad hoc networks
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