On Fault Classification in Connected Autonomous Vehicles Using Supervised Machine Learning
Bibliographic record
Abstract
Different health-monitoring techniques were considered in the literature to enhance the safety and stability of Connected Autonomous Vehicle (CAV) platoons. The health-monitoring processes include fault detection, localization, and mitigation. It is evident that mitigating these faults is faster and more reliable if the fault structure is known. To this end, we consider classifying the fault class using supervised machine learning. We first model a heterogeneous CAV platoon with three different common faults separately. These faults are bounded actuator disturbances (namely, engine bearing knock), False Data Injection (FDI) attack, and communication time delay. We consider two supervised machine learning classifiers, the first classifier determines whether the fault is bounded disturbances or communication delay, and the second classifier determines whether the disturbances are in the physical or cyber layer. We have compared four machine learning techniques for each classifier, Support Vector Machine (SVM), Naive Bayes (NB), Quadratic Discriminant (QD), and K-Nearest Neighbors (KNN). The classifiers are trained firstly on the simulation model, then are tested on a different set of observations and tested experimentally on a platoon of three autonomous robots. The highest accuracy was achieved by considering SVM for the first classifier and QD for the second classifier. The overall classification accuracy achieved is 96.8% for the simulation test and 92.1% for the experiment.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".