How to ensure control of cooperative vehicle and truck platoons using Meaningful Human Control
Bibliographic record
Abstract
Vehicle cooperation, not vehicle automation, will yield the greatest benefits on road traffic. However, satisfactory human control over platoons of cooperative vehicles still has a large number of uncertainties and issues to be addressed. This paper aims to address these broader issues of control over a cooperative vehicle platoon by focussing on a truck platooning system as a case example, and taking the perspective of Meaningful Human Control (MHC) as control concept. MHC goes further than mere operational control as it addresses issues that exist in current system designs, and proposes improvements based on a novel and more encompassing set of conditions for control. These issues are addressed in regard to the vehicles and their Operational Design Domains (ODD), the role and ability of the drivers (both leading and following) and how these exist in regard to their road environment. We conclude that current advances are making progress, but that from a MHC perspective, issues still remain for the operational and tactical implementation of truck platoons and cooperative driving that need to be addressed in regard to ODDs and drivers. Furthermore, consideration of responsibility and liability aspects is required that stretches beyond nominal appointment thereof, as this does not satisfy important ethical and societal standards. This is demonstrated in the paper through two hypothetical cases focussing on issues on a system level and one further analysis which is focussed on the role of the driver in the platooning system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".