Data-Driven Detection of Simultaneous Cyberphysical Faults in Connected Electric Vehicle Platoons
Bibliographic record
Abstract
Connected autonomous electric vehicles promise improved fuel efficiency and traffic flow through informed decision-making. However, vehicular networks present a wider cyberattack surface for malicious agents and increase the likelihood of simultaneous physical faults. Existing methods for health monitoring and fault diagnosis are inadequate to address this new environment. We propose a two-stage machine learning-based method to diagnose cyberphysical faults—our architecture consists of a lightweight binary classifier to perform the preliminary task of localization, and a multi-head attention module to perform classification. To generalize to arbitrary platoon sizes, we leverage the platoon topology and consider measurements from vehicle pairs. We develop a simulation framework utilizing low-level EV dynamics for the simulation and study of simultaneous faults. On a dataset generated using this framework, we show that our method achieves good performance compared with baseline methods, and we additionally demonstrate robustness to measurement noise.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".