Cooperative vehicular platooning: a multi-dimensional survey towards enhanced safety, security and validation

Filho, Ênio Vasconcelos; Severino, Ricardo; Santos, Pedro M.; Koubâa, Anis; Tovar, Eduardo

doi:10.1080/23335777.2023.2214584

Cited by 7 publications

(8 citation statements)

References 138 publications

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“…This concept involves autonomous vehicles working together in flexible platoons to enhance driving efficiency and safety. Recent research, as described in [ 84 ], has delved into advancements in Co-VP and focused on controlling strategies for vehicles, infrastructure communication, and cybersecurity. In [ 85 ], the study focused on investigating platoon cooperation in a multilane platoon scenario within a C-V2X network.…”

Section: Applicationsmentioning

confidence: 99%

Empowering the Vehicular Network with RIS Technology: A State-of-the-Art Review

Naaz,

Nauman,

Khurshaid

et al. 2024

Sensors

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Reconfigurable intelligent surfaces (RIS) are expected to bring about a revolutionary transformation in vehicular networks, thus paving the way for a future characterized by connected and automated vehicles (CAV). An RIS is a planar structure comprising many passive elements that can dynamically manipulate electromagnetic waves to enhance wireless communication by reflecting, refracting, and focusing signals in a programmable manner. RIS exhibits substantial potential for improving vehicle-to-everything (V2X) communication through various means, including coverage enhancement, interference mitigation, improving signal strength, and providing additional layers of privacy and security. This article presents a comprehensive survey that explores the emerging opportunities arising from the integration of RIS into vehicular networks. To examine the convergence of RIS and V2X communications, the survey adopted a holistic approach, thus highlighting the potential benefits and challenges of this combination. In this study, we examined several applications of RIS-aided V2X communication. Subsequently, we delve into the fundamental emerging technologies that are expected to empower vehicular networks, encompassing mobile edge computing (MEC), non-orthogonal multiple access (NOMA), millimeter-wave communication (mmWave), Artificial Intelligence (AI), and visible light communication (VLC). Finally, to stimulate further research in this domain, we emphasize noteworthy research challenges and potential avenues for future exploration.

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Section: Applicationsmentioning

confidence: 99%

Empowering the Vehicular Network with RIS Technology: A State-of-the-Art Review

Naaz,

Nauman,

Khurshaid

et al. 2024

Sensors

View full text Add to dashboard Cite

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“…Once the key fob reacts, the vehicle doors and even the engine can be unlocked. Remote access Practical attack Scalability is high Passive keyless entry systems compromised Vehicle unlocking Ignition system Immediate countermeasures: shielding the key, removing the battery from the key Midterm countermeasures: software-only modification, access control restriction, and hardware modification 5 3 3 4 5 4 V-TD10 : Data Analysis R, I Nonrepudiation, Confidentiality, and Privacy Cooperative Awareness Message (CAM) [ 37 ] Malicious advertisers(V2V/V2I) generate congestion response messages based on the content of the congestion requests [ 54 , 60 , 75 , 76 ] Proximity—remote access [ 37 ] Simulated attack [ 37 ] Scalability is high [ 37 ] The overall speed of CAVs could be affected. Increase in traffic in the targeted or neighboring roads.…”

Section: Table A1mentioning

confidence: 99%

“…Parked bots or compromised vehicles could be infected Correlation of messages from neighboring vehicles and cross-verification 5 4 4 5 3 4.2 T, R, I, E Integrity, Nonrepudiation, Confidentiality, Privacy, and Authorization Cooperative cruise control module Localization Vehicle control warning, e.g., lane departure warning Vehicle intersection warning Object identification Vehicle control automation Sensor fusion Fake env. conditions for camera lens [ 94 ] Inject fabricated frames directly into the camera processing and memory Infect camera firmware in order to generate fabricated frames indicating unintentional activities such as lane departure Frames are modified with property changes such as blurry images to confuse modeling software Insert fabricated frameworks and graphic models to indicate warnings Remove frames that indicate normal or abnormal conditions Deter/rush/delay delivery of speed, steering wheel info., status Inject code that processes frames and generates models or alters internal memory with fabricated frames [ 54 , 60 , 75 , 76 ] Inject code to fuse sensed data to indicate warnings and malicious outputs [ 71 , 94 , 105 , 131 ] Compromised cooperative cruise control functionalities leading to the following: An accident; The inability to activate functions; Driving into traffic; Discomfort. The following are the subimpacts: Sensor information altered; Sensor preprocessor manipulated; Main processor manipulated; Audio system exposed;...…”

Section: Table A1mentioning

confidence: 99%

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Securing Cloud-Assisted Connected and Autonomous Vehicles: An In-Depth Threat Analysis and Risk Assessment

Sheik,

Maple,

Epiphaniou

et al. 2023

Sensors

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As threat vectors and adversarial capabilities evolve, Cloud-Assisted Connected and Autonomous Vehicles (CCAVs) are becoming more vulnerable to cyberattacks. Several established threat analysis and risk assessment (TARA) methodologies are publicly available to address the evolving threat landscape. However, these methodologies inadequately capture the threat data of CCAVs, resulting in poorly defined threat boundaries or the reduced efficacy of the TARA. This is due to multiple factors, including complex hardware–software interactions, rapid technological advancements, outdated security frameworks, heterogeneous standards and protocols, and human errors in CCAV systems. To address these factors, this study begins by systematically evaluating TARA methods and applying the Spoofing, Tampering, Repudiation, Information disclosure, Denial of service, and Elevation of privileges (STRIDE) threat model and Damage, Reproducibility, Exploitability, Affected Users, and Discoverability (DREAD) risk assessment to target system architectures. This study identifies vulnerabilities, quantifies risks, and methodically examines defined data processing components. In addition, this study offers an attack tree to delineate attack vectors and provides a novel defense taxonomy against identified risks. This article demonstrates the efficacy of the TARA in systematically capturing compromised security requirements, threats, limits, and associated risks with greater precision. By doing so, we further discuss the challenges in protecting hardware–software assets against multi-staged attacks due to emerging vulnerabilities. As a result, this research informs advanced threat analyses and risk management strategies for enhanced security engineering of cyberphysical CCAV systems.

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A novel one-bit dynamic quantizer for event-triggered control systems

Almakhles,

Abdelrahim

2024

Information Sciences

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Cooperative vehicular platooning: a multi-dimensional survey towards enhanced safety, security and validation

Cited by 7 publications

References 138 publications

Empowering the Vehicular Network with RIS Technology: A State-of-the-Art Review

Empowering the Vehicular Network with RIS Technology: A State-of-the-Art Review

Securing Cloud-Assisted Connected and Autonomous Vehicles: An In-Depth Threat Analysis and Risk Assessment

A novel one-bit dynamic quantizer for event-triggered control systems

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