Security‐aware optimal actuator placement in vehicle platooning
Bibliographic record
Abstract
Abstract Vehicle platooning, as a large class of cyber‐physical systems, is prone to be under the risk of cyber attacks. One (or more) external intelligent intruder(s) might attack one (or more) of the vehicles participating in a platoon. This paper proposes a general approach to find an optimal actuator placement strategy according to the Stackelberg game between the attacker and the defender. The game payoff is the energy needed by the attacker to steer the consensus follower–leader dynamics of the system towards his desired direction. The attacker tries to minimize this energy while the defender attempts to maximize it. Thus, based on the defined game and its optimal equilibrium point, the defender(s) selects optimal actuator placement action to face the attacker(s). Both cases of single attacker–single defender and multiple attackers–multiple defenders cases are investigated. Furthermore, we study the effects of different information flow topologies, namely, the unidirectional and bidirectional data transfer structures. Besides, the impacts of increasing the connectivity among the nodes on the security level of the platoon are presented. Simulation results for h–nearest neighbor platoon formations along with experimental results using the scaled cars governed by robotic operating system (ROS) verify the effectiveness of the method.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 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".