Autonomous and Security-Aware Dynamic Vehicular Platoon Formation
Bibliographic record
Abstract
As vehicles increasingly embed digital systems, new security vulnerabilities are also being introduced. Computational constraints make it challenging to add security oversight layers on top of core vehicle systems, especially when the security layers rely on additional deep learning models for anomaly detection. To improve security-aware decision-making for autonomous vehicles (AV), this paper proposes a bi-level security framework. The first security level consists of a one-shot resource allocation game that enables a single vehicle to fend off an attacker by optimizing the configuration of its intrusion prevention system based on risk estimation. The second level relies on a reinforcement learning (RL) environment where an agent is responsible for forming and managing a platoon of vehicles on the fly while also dealing with a potential attacker. We solve the first problem using a minimax algorithm to identify optimal strategies for each player. Then, we train RL agents and analyze their performance in forming security-aware platoons. The trained agents demonstrate superior performance compared to our baseline strategies that do not consider security risk.
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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".