Regulating Autonomy: An Assessment of Policy Language for Highly Automated Vehicles
Bibliographic record
Abstract
Abstract Self‐driving cars (also known as driverless cars, autonomous vehicles, and highly automated vehicles [HAVs]) will change the regulatory, political, and ethical frameworks surrounding motor vehicles. At the highest levels of automation, HAVs are operated by independent machine agents, making decisions without the direct intervention of humans. The current transportation system assumes human intervention though, including legal and moral responsibilities of human operators. Has the development of these artificial intelligence (AI) and autonomous system (AS) technologies outpaced the ethical and political conversations? This paper examines discussions of HAVs, driver responsibility, and technology failure to highlight the differences between how the policy‐making institutions in the United States (Congress and the Public Administration) and technology and transportation experts are or are not speaking about responsibility in the context of autonomous systems technologies. We report findings from a big data analysis of corpus‐level documents to find that enthusiasm for HAVs has outpaced other discussions of the technology.
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 | 0.084 | 0.189 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".