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A systematic literature review on the relationship between autonomous vehicle technology and traffic-related mortality.
ํ์๋
ผ๋ฌธ(์์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ํ์ ๋ํ์ ๊ธ๋ก๋ฒํ์ ์ ๊ณต, 2023. 2. ์ตํํ.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.์์จ์ฃผํ์ฐจ(AV)๋ฅผ ํตํด ๊ตํต ํ๋ฆ์ด ๊ฐ์ ๋๊ณ ์ฌ๊ณ ๊ฐ ์ค์ด๋๋ ๋ฑ ์ฌํ๊ฐ ์ป๋ ๊ฒ์ด ๋ง์ ๊ฒ์ผ๋ก ์์๋๋ค. ๊ทธ๋ค์ ๋ค์ํ ์ธ๊ณต์ง๋ฅ(AI) ํ๋ก์ธ์ค์ ์ ๋ต์ ๊ฐ์ ์ ํฌ๊ฒ ์์กดํ๋ค. ์ด ๋ถ์ผ์ ์ผ๋ถ ์ฐ๊ตฌ์๋ค์ AV๊ฐ ์์ ์ฑ์ ํฅ์์ํค๋ ์ด์ ๋ผ๊ณ ๋ฏฟ์ง๋ง, ๋ค๋ฅธ ์ฐ๊ตฌ์๋ค์ AV๊ฐ ์ด๋ฌํ ์๋ก์ด ๊ธฐ์ /์์คํ
๋ฐ ์ ํ๋ฆฌ์ผ์ด์
์ ๋ณด์์ ๋ณด์ฅํ๋ ๊ฒ๊ณผ ๊ด๋ จํ์ฌ ์๋ก์ด ๋ฌธ์ ๋ฅผ ์ผ๊ธฐํ๋ค๊ณ ๋ฏฟ๋๋ค. ์ด ๋
ผ๋ฌธ์ ์์จ์ฃผํ์ฐจ ๊ธฐ์ ๊ณผ ๊ตํต ๊ด๋ จ ์ฌ๋ง๋ฅ ์ฌ์ด์ ๊ด๊ณ์ ๋ํ ์ฒด๊ณ์ ์ธ ๋ฌธํ ๊ฒํ ๋ฅผ ์ํํ๋ค. ํฌํจ ๋ฐ ์ ์ธ ๊ธฐ์ค์ ๋ฐ๋ผ EBSCO, ProQuest, IEEE Explorer ๋ฐ Web of Science์ ๊ธฐ์ฌ๋ฅผ ์ ํํ๊ณ ๋ถ๋ฅํ๋ค.์ฐ๊ตฌ ๊ฒฐ๊ณผ๋ ์ด๋ฌํ ์ถํ๋ฌผ์ ๋๋ถ๋ถ์ด ๊ณ ๊ธ ์ด์ก ๊ด๋ จ ์ ๋์ ๊ฒ์ฌ๋์์์ ๋ณด์ฌ์ค๋ค. ๋ฏธ๋์ ์๋์ฐจ ์ฐ์
์ ๊ฐ์ ๊ณผ ์ง๋ฅํ ๊ตํต ์์คํ
์ ๊ฐ๋ฐ์ ์น๋ช
์ ์ธ ๊ตํต ์ฌ๊ณ ์ ์๋ฅผ ์ค์ด๋ ๋ฐ ๋์์ด ๋ ์ ์๋ค. ์์จ์ฃผํ ์๋์ฐจ ๊ธฐ์ ์ ์ด์ ๊ฒฝํ์ ํฅ์์ํค๊ณ ๊ตํต ์ฌ๊ณ ์ ์๋ฅผ ์ค์ผ ์ ์๋ ํจ๊ณผ์ ์ธ ๋ฐฉ๋ฒ์ ์ ๊ณตํ๋ค. ์ถฉ๋, ๊ตํต, ์๋์ง ์ฌ์ฉ, ํ๊ฒฝ ์ค์ผ๊ณผ ๊ฐ์ ์๋ง์ ์ด์ ๊ด๋ จ ๋ฌธ์ ๋ค์ ์์จ ์ฃผํ ๊ธฐ์ ์ ์ํด ๋์์ ๋ฐ์ ๊ฒ์ด๋ค. ํ๊ฐ๋ ๋๋ถ๋ถ์ ์ฐ๊ตฌ์ ๋ํด ๋ ๋ง์ ์ฐ๊ตฌ๊ฐ ํ์ํ๋ค. ์ค์ ๋๋ ์ปดํจํฐ ์๋ฎฌ๋ ์ด์
์๋๋ฆฌ์ค, ๋ ์ข๊ณ ํ์ค์ ์ธ ์๋๋ฆฌ์ค, ๋ ์ข๊ณ ๋ ๋ง์ ๋ฐ์ดํฐ, ๊ทธ๋ฆฌ๊ณ ์ ์๋ ์ ๋ต ๊ฒฐ๊ณผ๊ฐ ์ฐ์
ํ์ค ๋ฐ ๊ฒฝ์ ์ ๋ต์ ๊ฒฐ๊ณผ์ ๋น๊ต๋๋ ์คํ ์ค๊ณ์์ ํ
์คํธ๋ ์ ์๋๋ก ํ์ฅ๋์ด์ผ ํ๋ค. ๋ฐ๋ผ์ ๊ฐ์ ๋ ๋ฐฉ๋ฒ์ ๋ํ ์ถ๊ฐ ์ฐ๊ตฌ๊ฐ ํ์ํ๋ค. ์ถ๊ฐ ์ฐ๊ตฌ๊ฐ ํ์ํ ๋ ๋ค๋ฅธ ์ฃผ์ ๋ถ์ผ๋ AV์ ๋๋์ , ์ค๋ฆฌ์ ์ ํ์ด๋ค. ์ ๋ถ, ์ ์ฑ
์
์์, ์ ์กฐ์
์ฒด ๋ฐ ์ค๊ณ์๋ ๋ชจ๋ ์์จ ์ฃผํ ์ฐจ๋์ ํจ๊ณผ์ ์ผ๋ก ๋๋ก์ ๋ฐฐ์นํ๊ธฐ ์ํด ๋ง์ ์กฐ์น๋ฅผ ์ทจํด์ผ ํ๋ค. ์ ๋ถ๋ ํนํ ๋ฒ, ๊ท์น, ์คํ ๊ณํ์ ๊ฐ๋ฐํด์ผ ํ๋ค. ์ด์ ์ ์๋ ์ฐจ๋๊ณผ ๊ฐ์ ์ด์ก ์์คํ
์์ ์๋ก์ด ๊ธฐ์ ์ ์ฑํ์ ์ฅ๋ คํ ์ ์๋ ๋ณด๋ค ํจ๊ณผ์ ์ธ ํ๋ก๊ทธ๋จ์ ๋ง๋๋ ๊ฒ์ด ์ค์ํ๋ค. ์ด์ ๊ด๋ จํ์ฌ, ์ค๊ณ์์๊ฒ ํ์ฌ ์ด์์ ์ฌ๋์ ์ํ ๊ด์ฐฐ์ ์๋ ค์ค ์ ์๊ธฐ ๋๋ฌธ์ ์ฌ์ฉ์ ์ธ์์ด ํ์์ ์ด ๋๋ค.์ ์กฐ์
์ฒด๋ ์์คํ
๊ฒฐํจ์ ์์ ํด์ผ ํ๋ฉฐ ํจ์จ์ ์ธ ์๋์ ์ํด ์ข์ ๋ฐ์ดํฐ ์ธํธ๊ฐ ํ์ํ๋ค. ๋ฉ์ง ์์ ๋ฏธ๋์, ๊ณ ๋๋ก ์๋ํ๋ ์ฐจ๋(AV)์ ๊ด๋ฒ์ํ ์ฌ์ฉ์ ํ์ฌ์ ์์ ๊ด๋ จ ์ฐ๊ตฌ์์ ์ง์์ ์ธ ๋ฌธ์ ๋ฅผ ํด๊ฒฐํ๊ธฐ ์ํ ํฅ๋ฏธ๋ก์ด ์๋ก์ด ๊ฐ๋ฅ์ฑ์ ์ด์ด์ค ์ ์๋ค. ๋ณด๊ธ๋ฅ , ๊ณต๊ณต ์ฑํ, ๊ธฐ์ ๋ฐ์ , ๊ตํต ํจํด ๋ฐ ๋น์ฆ๋์ค ๋ชจ๋ธ๊ณผ ๊ฐ์ ์์๋ฅผ ๊ณ ๋ คํ์ฌ Avs์ ์ค์ํ ์ ์ฑ
์ํฅ์ ๋ ์ ์ดํดํ๊ณ ์ ๋ํํ๊ธฐ ์ํ ์ถ๊ฐ ์ฐ๊ตฌ๊ฐ ํ์ํ๋ค. ์กฐ์ฌ๋ฅผ ์ํด ๋๋ฃ ๊ฒํ ๋ฅผ ๊ฑฐ์น ์ ๋ฌธ ์ ๋ ๋
ผ๋ฌธ๋ง ๊ณ ๋ คํ๋ฉด ๋์ง๋ง, ๋ฐ์ดํฐ๋ฒ ์ด์ค๊ฐ ์ปค์ง๊ณ ๋ฌธ์๊ฐ ๋ง์์ง๋ฉด ๋ ๋ง์ ๊ฒฐ๊ณผ์ ๋ ์ฒ ์ ํ ๋ถ์์ด ์ ๊ณต๋ ๊ฒ์ด ๋ถ๋ช
ํ๋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
Automated Algorithmic Machine-to-Machine Negotiation for Lane Changes Performed by Driverless Vehicles at the Edge of the Internet of Things
This dissertation creates and examines algorithmic models for automated machine-to-machine negotiation in localized multi-agent systems at the edge of the Internet of Things. It provides an implementation of two such models for unsupervised resource allocation for the application domain of autonomous vehicle traffic as it pertains to lane changing and speed setting selection. The first part concerns negotiation via abstract argumentation. A general model for the arbitration of conflict based on abstract argumentation is outlined and then applied to a scenario where autonomous vehicles on a multi-lane highway use expert systems in consultation with private objectives to form arguments and use them to compete for lane positions. The conflict resolution component of the resulting argumentation framework is augmented with social voting to achieve a community supported conflict-free outcome. The presented model heralds a step toward independent negotiation through automated argumentation in distributed multi-agent systems. Many other cyber-physical environments embody stages for opposing positions that may benefit from this type of tool for collaboration. The second part deals with game-theoretic negotiation through mechanism design. It outlines a mechanism providing resource allocation for a fee and applies it to autonomous vehicle traffic. Vehicular agents apply for speed and lane assignments with sealed bids containing their private feasible action valuations determined within the context of their governing objective. A truth-inducing mechanism implementing an incentive-compatible strategyproof social choice functions achieves a socially optimal outcome. The model can be adapted to many application fields through the definition of a domain-appropriate operation to be used by the allocation function of the mechanism. Both presented prototypes conduct operations at the edge of the Internet of Things. They can be applied to agent networks in just about any domain where the sharing of resources is required. The social voting argumentation approach is a minimal but powerful tool facilitating the democratic process when a community makes decisions on the sharing or rationing of common-pool assets. The mechanism design model can create social welfare maximizing allocations for multiple or multidimensional resources
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Knowledge Discovery and Data Mining for Shared Mobility and Connected and Automated Vehicle Applications
The rapid development of shared mobility and connected and automated vehicles (CAVs) has not only brought new intelligent transportation system (ITS) challenges with the new types of mobility, but also brought a huge opportunity to accelerate the connectivity and informatization of transportation systems, particularly when we consider all the new forms of data that is becoming available. The primary challenge is how to take advantage of the enormous amount of data to discover knowledge, build effective models, and develop impactful applications. With the theoretical and experimental progress being made over the last two decades, data mining and machine learning technologies have become key approaches for parsing data, understanding information, and making informed decisions, especially as the rise of deep learning algorithms bringing new levels of performance to the analysis of large datasets. The combination of data mining and ITS can greatly benefit research and advances in shared mobility and CAVs.This dissertation focuses on knowledge discovery and data mining for shared mobility and CAV applications. When considering big data associated with shared mobility operations and CAV research, data mining techniques can be customized with transportation knowledge to initially parse the data. Then machine learning methods can be used to model the parsed data to elicit hidden knowledge. Finally, the discovered knowledge and extracted information can help in the development of effective shared mobility and CAV applications to achieve the goals of a safer, faster, and more eco-friendly transportation systems.In this dissertation, there are four main sections that are addressed. First, new methodologies are introduced for extracting lane-level road features from rough crowdsourced GPS trajectories via data mining, which is subsequently used as the fundamental information for CAV applications. The proposed method results in decimeter level accuracy, which satisfies the positioning needs for many macroscopic and microscopic shared mobility and CAV applications. Second, macroscopic ride-hailing service big data has been analyzed for demand prediction, vehicle operation, and system efficiency monitoring. The proposed deep learning algorithms increase the ride-hailing demand prediction accuracy to 80% and can help the fleet dispatching system reduce 30% of vacant travel distance. Third, microscopic automated vehicle perception data has been analyzed for a real-time computer vision system that can be used for lane change behavior detection. The proposed deep learning design combines the residual neural network image input with time serious control data and reaches 95% of lane change behavior prediction accuracy. Last but not least, new ride sharing and CAV applications have been simulated in a behavior modeling framework to analyze the impact of mobility and energy consumption, which addresses key barriers by quantifying the transportation system-wide mobility, energy and behavior impacts from new mobility technologies using real-world data
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