Advanced Adaptive Control Strategies for Fault Tolerance and Cybersecurity in Autonomous Vehicles
Bibliographic record
Abstract
As autonomous vehicles play an increasingly vital role in critical sectors such as transportation, logistics, and defense, ensuring their operational safety and security becomes paramount. These vehicles are susceptible to both physical faults, such as actuator failures, and cyberattacks, including denial-of-service, deception, and replay attacks. Traditional fault-tolerant control (FTC) systems focus on physical faults but often overlook cyber threats that can compromise vehicle safety and performance. This article presents an adaptive FTC framework that integrates an intrusion detection and protection system with control design to address these challenges. The framework enables real-time threat detection and dynamic control adaptation to ensure vehicle resilience under compromised conditions. The effectiveness of this approach is demonstrated through real-time experiments with Quanser QCar vehicles, confirming its capability to mitigate cyber–physical threats. This work advances secure autonomous systems by providing a comprehensive solution addressing both physical and cyber vulnerabilities, thereby enhancing overall safety and security.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".