Using Governance and Adaptive Normative Multiagent Systems for Dynamic Vehicle Platoon Formation
Bibliographic record
Abstract
Autonomous vehicles (AVs) are vehicles able to sense their environments and guide themselves with little or no human input. AVs have the potential to provide a wide variety of benefits to road traffic, such as improving traffic flow, alleviating traffic congestion and reducing car accidents. However, there are scenarios where AVs require governance systems capable of performing collaborative actions in order to keep traffic flowing smoothly and safely. This paradigm shift will support moving from isolated to collaborative autonomous vehicles. Our goal is to create a collaborative governance-based autonomous vehicle approach using adaptive normative multiagent systems to reduce congestion time. With this collaborative perspective in mind, steps should be taken for AVs to improve their collective mobility while not undermining the `social' goals of vehicle platoons and be able to provide benefits such as reduced congestion, increased travel choice and equity, and reduced pollutant emissions.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".