Artificial intelligence (AI) empowered anomaly detection for autonomous vehicles in 6G-V2X

The development of advanced Intelligent Transportation Systems has been made possible by the rapid expansion of autonomous vehicles (AVs) and networking technology (ITS). The in-vehicle users' increased data needs from AVs put the vehicle's trajectory data in danger and make it more suscep...

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Bibliographic Details
Published in:	Mehran University research journal of engineering and technology Vol. 42; no. 3; pp. 79 - 88
Main Authors:	Kandhro, Irfan Ali, Ali, Fayyaz, Panhwar, Ali Orangzeb, Ahmed Larik, Raja Sohail, Fatima, Kanwal
Format:	Journal Article
Language:	English
Published:	Mehran University of Engineering and Technology 01-07-2023
Subjects:	Algorithms Artificial intelligence Driverless cars Sensors
Online Access:	Get full text
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Summary:	The development of advanced Intelligent Transportation Systems has been made possible by the rapid expansion of autonomous vehicles (AVs) and networking technology (ITS). The in-vehicle users' increased data needs from AVs put the vehicle's trajectory data in danger and make it more susceptible to security threats. In this paper, Autonomous vehicles (AVs) transform the intelligent transportation system by exchanging real-time and seamless data with other AVs and the network (ITS). Transportation that is automated has many advantages for people. However, worries about safety, security, and privacy continue to grow. The AVs need to exchange sensory data with other AVs and with their own for navigation and trajectory planning. When an unreliable sensor-equipped AV or one that is malicious enters connectivity in such circumstances, the results could be disruptive. To effectively detect anomalies and mitigate cyberattacks in AVs, this study suggests the Efficient Anomaly Detection (EAD) method. The EAD technique finds and isolates rogue AVs using the Multi-Agent Reinforcement Learning (MARL) algorithm, which operates over the 6G network to thwart modern cyberattacks and provide a quick and accurate anomaly detection mechanism. The expected outcomes demonstrate the value of EAD and have an accuracy rate that is 8.01% greater than that of the current systems.
ISSN:	0254-7821 2413-7219
DOI:	10.22581/muet1982.2303.09