Human-Assisted Resilient Coordination for Automated Platoon With False Data Injection Attack
Bibliographic record
Abstract
This paper investigates the resilient platooning problem of connected and automated vehicles (CAVs) under false-data injection (FDI) attacks. These attacks can alter the system state of any vehicle within a platoon at any time, presenting a significant challenge. To tackle this, we introduce a human-assisted resilient coordination (HARC) approach for CAVs, comprised of three core modules: a joint attack detection mechanism, a human-assisted coordination scheme, and a distributed optimization-based control module. The attack detection approach monitors vehicles at two distinct levels - local communication and global communication. Our human-assisted coordination strategy is designed not only to respond efficiently to attacks but also to ensure seamless coordination of the vehicle platoon, thereby preventing accidents and enhancing flexibility. By incorporating human input, we bolster the resilience and security of platoon coordination. Moreover, we present a distributed optimization-based control technique tailored specifically for constrained CAVs. A resilience constraint is designed and incorporated into the optimization problem, thereby empowering CAVs to identify adversarial communication channels via a localized communication detector. We provide experimental results to showcase the effectiveness of our proposed approach and to highlight its implications for resilient platooning under FDI attacks.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".