Cybersecurity of Autonomous Vehicles — A Detailed Research Paper
Abstract
Autonomous vehicles (AVs) combine advanced sensing, machine learning, networking, and control so tightly that cyber vulnerabilities can directly translate into physical harm. This paper surveys the current state of AV cybersecurity: system architecture and attack surface, threat taxonomy (including remote attacks, sensor manipulation, adversarial machine-learning attacks, and supply-chain risks), representative incidents, standards and regulatory responses, defenses at hardware/software/operations levels, open research problems, and actionable recommendations for industry and regulators. Key load-bearing facts—standards (ISO/SAE 21434, UNECE WP.29), NHTSA guidance, real-world safety incidents involving ADAS/Autopilot, and the prevalence of adversarial/physical attacks on perception—are documented and cited. ScienceDirect+4ISO+4UNECE+4
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