Formal Methods and Validation Techniques for Ensuring Automotive Systems Security

Information, Journal Year: 2023, Volume and Issue: 14(12), P. 666 - 666

Published: Dec. 18, 2023

The increasing complexity and connectivity of automotive systems have raised concerns about their vulnerability to security breaches. As a result, the integration formal methods validation techniques has become crucial in ensuring systems. This survey research paper aims provide comprehensive overview current state-of-the-art employed industry for system security. begins by discussing challenges associated with potential consequences Then, it explores various methods, such as model checking, theorem proving, abstract interpretation, which been widely used analyze verify properties Additionally, highlights ensure effectiveness measures, including penetration testing, fault injection, fuzz testing. Furthermore, examines within development lifecycle, requirements engineering, design, implementation, testing phases. It discusses benefits limitations these approaches, considering factors scalability, efficiency, applicability real-world Through an extensive review relevant literature case studies, this provides insights into trends, challenges, open questions field findings can serve valuable resource researchers, practitioners, policymakers involved development, evaluation secure

Language: Английский

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AI Safety and Security

Advances in computational intelligence and robotics book series, Journal Year: 2024, Volume and Issue: unknown, P. 354 - 383

Published: Aug. 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.

Language: Английский

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

Academia Engineering, Journal Year: 2025, Volume and Issue: 2(2)

Published: April 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.

Language: Английский

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

Journal of Machine and Computing, Journal Year: 2025, Volume and Issue: unknown, P. 1084 - 1098

Published: April 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.

Language: Английский

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Design, construction, and recent advancements in temporal knowledge graphs for automated driving

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 205 - 226

Published: Jan. 1, 2025

Language: Английский

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