Security and Resilience in Autonomous Vehicles: A Proactive Design Approach

2026-04-14

AI Summary

This study addresses the vulnerability of autonomous driving systems to cyber-physical attacks that jeopardize their safety and operational continuity. The authors propose a hierarchical attack taxonomy and develop a resilient architecture integrating redundancy, diversity, and adaptive reconfiguration, uniquely bridging layered threat modeling with practical defense mechanisms. Key innovations include an intrusion detection method combining anomaly detection and hashing, a depth-camera-specific anti-blinding strategy, tamper-resistant software design for perception modules, and enhanced V2X communication security. Experimental validation on the Quanser QCar platform demonstrates the approach’s effectiveness in detecting depth camera blinding and perception software tampering attacks, significantly improving system resilience, operational continuity, and safety under adversarial conditions.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Multiagent Systems: Adversarial Agents
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Cyber-crime defenses and forensics

Abstract

Autonomous vehicles (AVs) promise efficient, clean and cost-effective transportation systems, but their reliance on sensors, wireless communications, and decision-making systems makes them vulnerable to cyberattacks and physical threats. This chapter presents novel design techniques to strengthen the security and resilience of AVs. We first provide a taxonomy of potential attacks across different architectural layers, from perception and control manipulation to Vehicle-to-Any (V2X) communication exploits and software supply chain compromises. Building on this analysis, we present an AV Resilient architecture that integrates redundancy, diversity, and adaptive reconfiguration strategies, supported by anomaly- and hash-based intrusion detection techniques. Experimental validation on the Quanser QCar platform demonstrates the effectiveness of these methods in detecting depth camera blinding attacks and software tampering of perception modules. The results highlight how fast anomaly detection combined with fallback and backup mechanisms ensures operational continuity, even under adversarial conditions. By linking layered threat modeling with practical defense implementations, this work advances AV resilience strategies for safer and more trustworthy autonomous vehicles.

Problem

• Autonomous Vehicles
• Security
• Resilience
• Cyberattacks
• Threat Modeling

Innovation

• resilient architecture
• anomaly detection
• V2X security
• adaptive reconfiguration
• autonomous vehicle security