Smart Intersections and Connected Autonomous Vehicles for Sustainable Smart Cities: A Brief Review DOI Open Access

Masoud Khanmohamadi,

Marco Guerrieri

Sustainability, Год журнала: 2025, Номер 17(7), С. 3254 - 3254

Опубликована: Апрель 5, 2025

As the importance of safety, efficiency, and sustainability in urban transportation becomes more apparent, intelligent systems are changing growing. Smart intersections play a crucial role different parts this context. Technologies such as Vehicle-to-Everything (V2X) communication, artificial intelligence, multi-sensor data fusion, incorporated into these to improve capacity safety reduce damage environment. This literature review aims merge various recent works on advancing smart intersection technologies, their thematic application, methodological approach, regional implementations. Highlighting adaptive traffic signal control, real-time processing, connected autonomous vehicle (CAV) integrations sheds light way effectiveness cities can be improved. At same time, study tackles questions cybersecurity standardization. provides insights for researchers, policymakers, practitioners who aim systems’ sustainability, fairness, operability.

Язык: Английский

Sybil Attacks Detection and Traceability Mechanism Based on Beacon Packets in Connected Automobile Vehicles DOI Creative Commons
Yaling Zhu, Jia Zeng,

Fangchen Weng

и другие.

Sensors, Год журнала: 2024, Номер 24(7), С. 2153 - 2153

Опубликована: Март 27, 2024

Connected Automobile Vehicles (CAVs) enable cooperative driving and traffic management by sharing information between them other vehicles infrastructures. However, malicious create Sybil forging multiple identities false location with CAVs, misleading their decisions behaviors. The existing work on defending against attacks has almost exclusively focused detecting vehicles, ignoring the traceability of vehicles. As a result, they cannot fundamentally alleviate attacks. In this work, we focus tracking attack source using novel detection mechanism that relies vehicle broadcast beacon packets. Firstly, roadside units (RSUs) randomly instruct to perform customized key broadcasting listening within communication range. This allows prove its physical presence broadcasting. Then, RSU analyzes packets listened constructs neighbor graph based particular fields in Finally, vehicle’s credibility is determined calculating edge success probability graph, ultimately achieving tracing experimental results demonstrate our scheme achieves real-time precision recall rates 98.53% 95.93%, respectively, solving challenge schemes failing combat from root.

Язык: Английский

Процитировано

4
AI Safety and Security DOI
Mosiur Rahaman, Princy Pappachan,

Sheila Mae Orozco

и другие.

Advances in computational intelligence and robotics book series, Год журнала: 2024, Номер unknown, С. 354 - 383

Опубликована: Авг. 15, 2024

The chapter “AI Safety and Security” presents a comprehensive multi-dimensional exploration, addressing the critical aspects of safety security in context large language models. begins by identifying risks threats posed LLMs, delving into vulnerabilities such as bias, misinformation, unintended AI interactions, impacts like privacy concerns. Building on these identified risks, it then explores strategies methodologies for ensuring safety, focusing principles robustness, transparency, accountability discussing challenges implementing measures. It concludes with an insight long-term research, highlighting ongoing efforts future directions to sustain system amidst rapid technological advancements encouraging collaborative approach among various stakeholders. By integrating perspectives from computer science, ethics, law, social sciences, provides insightful analysis current security.

Язык: Английский

Процитировано

3
Strategies for Combating Criminal Use and Abuse of Artificial Intelligence DOI
Kathirvel Ayyaswamy, Naren Kathirvel,

Maria Manuel Vianny

и другие.

Advances in marketing, customer relationship management, and e-services book series, Год журнала: 2025, Номер unknown, С. 257 - 282

Опубликована: Янв. 10, 2025

This study investigates the criminal use and abuse of artificial intelligence (AI), exploring effectiveness various mitigation strategies. It employs a mixed-methods approach, combining quantitative data from survey 211 experts with qualitative insights academic, governmental, industrial publications. The research examines four key hypotheses: impact public organizational awareness, role advanced detection technologies, ethical guidelines, influence penalties enforcement. findings reveal that technology, ethics, enforcement all contribute to mitigating AI misuse. concludes by proposing comprehensive strategies, including targeted awareness campaigns, investment in robust strengthened legal frameworks, effectively combat AI.

Язык: Английский

Процитировано

0
Exploring Autonomous Vehicle Technology: Advancements, Challenges, and the Critical Role of Simulation DOI
Leila Haj Meftah, Asma Cherif

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Investigation of Quantum Machine Learning for Smart Eco System Focusing on Energy Optimization DOI
Sajjad Hussain, Nishit Malviya, Prakash Pareek

и другие.

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
High-Performance Data Throughput Analysis in Wireless Ad Hoc Networks for Smart Vehicle Interconnection DOI Creative Commons
Alaa Kamal Yousif Dafhalla, Amira Elsir Tayfour Ahmed, Nada Mohamed Osman Sid Ahmed

и другие.

Computers, Год журнала: 2025, Номер 14(2), С. 56 - 56

Опубликована: Фев. 10, 2025

Vehicular Ad Hoc Networks play a crucial role in enabling Smart City applications by facilitating seamless communication between vehicles and infrastructure. This study evaluates the throughput performance of different routing protocols, specifically AODV, AODV:TOM, AODV:DEM, GPSR, GPSR:TOM, GPSR:DEM, under various city highway scenarios complex networks. The analysis covers key parameters including traffic generation, packet sizes, mobility speeds, pause times. Results indicate that TOM DEM profiles significantly improve compared to traditional AODV GPSR protocols. GPSR:TOM achieves highest across most scenarios, making it promising solution for high-performance data transmission Cities. For instance, an average 3.2 Mbps 2.8 while increases 3.6 Mbps. Additionally, AODV:DEM records 3.4 high outperforming AODV:TOM at 3.1 baseline 2.7 findings highlight importance optimizing ensure reliability efficiency vehicle interconnection systems, which are critical management, accident prevention, real-time smart urban environments.

Язык: Английский

Процитировано

0
A Legal Study: How Do China’s Top 10 Intelligent Connected Vehicle Companies Protect Consumer Rights? DOI Creative Commons

Tian Sun,

Yao−Zhong Xu,

Hanbin Wang

и другие.

World Electric Vehicle Journal, Год журнала: 2025, Номер 16(3), С. 140 - 140

Опубликована: Март 2, 2025

This paper presents a case study on intelligent connected vehicle data. Intelligent vehicles (ICVs) gather comprehensive road data throughout operation to facilitate automation and enhance user experiences. However, this technological innovation new concerns for security privacy. employs analysis examine the protection provisions of top ten ICV companies in China governmental rules pertaining utilization. The findings indicate that these organizations do not completely adhere legal rights afforded consumers, resulting possible vulnerabilities. To improve situation, Chinese government ought explicitly specify regulatory responsibilities National Security Council (NSC) Ministry Industry Information Technology (MIIT) via regulations. Furthermore, should use media educate public about their rights. These initiatives seek aid promptly updating legislation efficiently controlling breach threats as ICVs increase.

Язык: Английский

Процитировано

0
Adaptive Fuzzy Logic Control Framework for Aircraft Landing Gear Automation: Optimized Design, Real-Time Response, and Enhanced Safety DOI Creative Commons
Idriss Dagal, Wulfran Fendzi Mbasso,

Harrison Ambe

и другие.

International Journal of Aeronautical and Space Sciences, Год журнала: 2025, Номер unknown

Опубликована: Март 19, 2025

Язык: Английский

Процитировано

0
Advancements in computer vision for safer overtaking: a review of deep learning methods DOI

Poma Panezai,

Hania Batool,

Dar Ghulam Raza

и другие.

Academia Engineering, Год журнала: 2025, Номер 2(2)

Опубликована: Апрель 3, 2025

Road traffic accidents are a common global issue, causing injuries, fatalities, and substantial economic losses. Additionally, overtaking vehicle is one of the leading causes car collisions. Therefore, ensuring safe vehicles critical concern. With advancement Artificial Intelligence its implementation in vehicles, many solutions have been proposed to tackle this problem. This review article explores image processing deep learning techniques that enhance safety on roadways. It provides comprehensive overview methodologies advancements computer vision, mainly focusing using neural networks analyze interpret real-time visual data facilitate taking efficient secure decisions vehicular scenarios. also examines traditional approaches maneuvers highlights their inherent limitations. Subsequently, delves into important role recognizing potential risks overtaking, which helps make driving safer. Furthermore, discusses possible future directions field identifies areas require further research.

Язык: Английский

Процитировано

0
Real-Time Autonomous Vehicle Automation With 5G-Based Edge Computing and Artificial Intelligence DOI

D. Geethanjali,

N. Minh, Prasanna Kumar Lakineni

и другие.

Journal of Machine and Computing, Год журнала: 2025, Номер unknown, С. 1084 - 1098

Опубликована: Апрель 5, 2025

Autonomous Vehicles (AV) are revolutionizing transportation, but real-time decision-making remains a challenge due to End-To-End Delay (EED introduced by Cloud Computing (CC) based processing. A 5G-enabled Edge Model (5G-EECM) is proposed address this problem processing time-sensitive tasks at the network edge, closer AV, reducing EED and improving responsiveness. The architecture uses Machine Learning (ML) for Obstacle Detection (OD) Reinforcement (RL) navigation, dynamically switching between (EC) EC CC on task demands. study tested system using user-friendly AV controlled track, revealing increased response times, reduced average EED, energy consumption, improved OD accuracy. results demonstrate that 5G-EECM significantly boosts systems' safety efficiency, making it reliable scalable next-generation systems.

Язык: Английский

Процитировано

0