Mock Trial: Human Factors Contributions to Litigation Involving Adaptive Cruise Control
Bibliographic record
Abstract
A mock trial format will be used to explore some fundamental human factors issues associated with advanced cruise control systems such as have been introduced in Europe and Japan and are expected to be introduced into the North American market this year. The plaintiff in this case, the driver of a vehicle equipped with ACC, is seeking damages from the defendant, the manufacturer of the vehicle, for inappropriate design of the ACC that she alleges contributed to a motor vehicle collision in which she was involved. The underlying issue concerns the hand-over of control from the vehicle to the driver under conditions of partially automated driving. The mock trial will demonstrate the role of human factors expertise in the judicial process. Participants will include experienced human factors professionals and practicing attorneys. Commentators will highlight key issues during the proceedings. No judgment will be rendered at the conclusion. However, delegates will be surveyed to determine how human factors expert opinions may have influenced them and which arguments were most compelling.
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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 | 0.042 | 0.213 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.021 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".