Safeguard Vehicle Platooning Based on Resilient Control Against False Data Injection Attacks
Bibliographic record
Abstract
This paper investigates secure control for homogeneous vehicle platoons in the presence of false data injection attacks with low communication and computation costs. We consider a scenario where each vehicle within the platoon transmits a local state vector to multiple neighboring vehicles. By leveraging these shared vectors from both preceding and following vehicles, we propose a novel and effective resilient controller for vehicle platoons against node/communication link attacks. More specifically, each vehicle determines the local state deviation vectors from neighboring vehicles. It then eliminates the vectors that are farthest from the origin, with the number of removed vectors equivalent to the maximum number of attacks. This approach offers a considerable advantage by mitigating the effects of abnormality and manipulation, making it robust against arbitrary information tampering within a pre-defined upper boundary for manipulated broadcast information. Importantly, we establish specific conditions for the proposed resilient design to guarantee the internal stability of the vehicle platoon under attacks. Extensive simulations and experiments involving four TurtleBot3s are conducted to demonstrate the effectiveness of the proposed resilient controller.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".