Human factors of automated driving systems: a compendium of lessons learned
Bibliographic record
Abstract
On-road deployment of partial automation and testing of vehicles with higher levels of automated driving systems have been ongoing for several years. Recent research partly confirms what we already knew about user interaction with automation in aviation, and, interestingly, adds relevant information to our understanding of human operators' adoption of vehicle technology. In this study, we review key studies from the last quinquennial on driver interaction with partial and higher levels of automation, with the goal of providing a compendium for transportation professionals and legislators. In addition to providing a brief but necessary introduction of the Society of Automotive Engineers Taxonomy, we address research findings and human factors safety takeaways for partial automation. Our review shows that driver underload, lacking mental models, and driver training are key issues that merit further human factors investigation. In the latter part of the compendium, we also discuss recent findings and policy considerations on higher levels of automated driving systems. These include developing more transparent and comprehensive ways of reporting incidents and system disengagements, having protocols that help minimise safety risks during transitions of control, and implementing validation methods that help mitigate the safety risks of automated systems.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".