Cyber-Physical Fault Detection in Autonomous Electric Vehicle Platoons with Lateral Motion
Bibliographic record
Abstract
Health monitoring systems for detecting and clas-sifying cyber-physical faults are essential for connected au-tonomous vehicle (CAV) platoons, given the vulnerabilities associ-ated with their wireless communication and information-sharing capabilities. Various model-based, signal-based, and data-driven methods have been developed for CAV health monitoring in the literature. However, research considering lateral dynamics and associated lateral manoeuvres, such as lane changes, is still limited. This paper discusses the importance of considering lateral dynamics in the context of CAV cyber-physical fault detection and classification, and investigates the use of multi-head attention (MHA) and graph neural network (GNN) models for this purpose. Our results demonstrate that attention-based classifiers outperform other methods in most scenarios, achieving approximately 73% accuracy. In contrast, while GNN-based methods like the graph attention network capture localized dependencies in simplified cyber-attack cases, their performance remains lower at around 65 %. These findings indicate the need for further research on robust attack classifiers and integrated fault detection frameworks for complex cyber-physical fault scenarios in electric vehicle platoon operations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".