Transportation Ergonomics for Self-Driving Automated Vehicles: Out-dated or Necessary?
Bibliographic record
Abstract
While automated vehicles are emerging around the world, many people are unsure what role, if any, ergonomics will play in this new technology. Will ergonomics still be necessary for automated vehicles? This article attempts to answer this question by reviewing key information on two aspects. First, the key elements of ergonomics in human-driven vehicles, including vehicle ergonomics, warehouse ergonomics, training and education, and research and profession, are described. Second, the characteristics of automated vehicles on highways and off highways (e.g. mining and agriculture) are discussed. These two aspects provide the insight needed to determine whether ergonomics are still necessary for automated vehicles and to determine the level of ergonomic requirements. The reader may be surprised to know that self-driving automated vehicles must have drivers, although some special-purpose automated vehicles will be driverless. The results indicate that self-driving automated vehicles have the same ergonomic requirements as human-driven vehicles and that their automated features warrant even more ergonomic research. The results also indicate that ergonomic applications are needed for emerging automated warehouses and off-highway automated vehicles.
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 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".