Fault Tolerance and Fallback Strategies in Connected and Automated Vehicles: A Review

Rodríguez-Arozamena, Mario; Matute, Jose; Araluce, Javier; Pérez Rastelli, Joshué; Zubizarreta, Asier · 2025 · Crossref

DOI: 10.1109/ojits.2025.3583787

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Summary

This review paper addresses the critical safety challenges associated with Connected and Automated Vehicles (CAVs), specifically focusing on fault tolerance and Dynamic Driving Task (DDT) fallback strategies. The motivation stems from the high prevalence of traffic accidents caused by human error and the inherent vulnerabilities of CAVs to system failures, environmental uncertainties, and functional insufficiencies. As CAVs advance toward higher automation levels (SAE Level 3+), ensuring resilience against these hazards becomes a regulatory and technical imperative. The authors identify a gap in existing literature, noting that while reviews exist for specific subsystems, no comprehensive study categorizes DDT fallback and fault tolerance strategies from a unified taxonomy perspective. The study employs a systematic literature review methodology, searching databases such as IEEE Xplore, Web of Science, and Google Scholar between March 2023 and April 2025. The authors establish a unified taxonomy based on international standards, including SAE J3016, ISO 26262, ISO 21448, and ISO/TR 4804. This framework distinguishes between causes of emergencies (system failures and functional insufficiencies) and handling strategies (fail-safe, fail-degraded, and fail-operational). The review categorizes identified strategies across eight domains: sensors, ego-vehicle localization, perception, decision/planning, control, communication, driver-vehicle interaction, and vehicle dynamics. Key findings reveal distinct trends in handling specific failure modes. For localization, particularly GNSS faults, common strategies include sensor fusion using Kalman filtering, switching to independent localization pipelines (e.g., LiDAR), and employing AI-based odometry. In perception, AI-based methods such as Convolutional Neural Networks (CNNs) and Reinforcement Learning are used to predict failures and reconfigure fusion algorithms. Decision and planning modules frequently utilize backup trajectory provision and Model Predictive Control to manage uncertainties and ensure safe stops. Control strategies often rely on fault-tolerant control techniques, including Lyapunov theory and control allocation. Communication-related strategies include remote driving (teleoperation) as a fallback, though latency remains a significant risk. The review also highlights cybersecurity threats, such as GNSS jamming and spoofing, as critical failure vectors. The significance of this work lies in its clarification of inconsistent terminology and its comprehensive categorization of fault tolerance regimes. The analysis indicates a prevailing industry trend toward avoiding the termination of CAV operations, favoring strategies that allow continued operation in degraded or operational modes rather than immediate safe stops. This shift supports the development of higher automation levels, particularly Level 4, where the ADS must independently manage failures. The paper concludes by identifying future research directions, emphasizing the need for standardized validation environments and improved handling of functional insufficiencies to enhance the overall safety and reliability of automated driving systems.

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