Automated Vehicle Disengagement Reaction Time Compared to Human Brake Reaction Time in Both Automobile and Motorcycle Operation
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Autonomous Vehicle Disengagement Reports have been published by the California Department of Motor Vehicles since 2015. The State of California autonomous vehicle testing regulations require every manufacturer authorized to test autonomous vehicles on public roads to submit an annual report summarizing disengagements. In early versions of the annual report, automated vehicle manufacturers were required to provide the time that it took for a human driver to take manual control of the vehicle when reporting vehicle disengagements. This study analyzes reported automated vehicle disengagement reaction times from 2015 through 2017 for various manufacturers that provided information to the California Department of Motor Vehicles while operating vehicles in autonomous mode. This study compares the reported automated vehicle operation disengagement reaction time to field literature in testing and experimentation on human brake reaction times for both automobile and motorcycle operation.</div></div>
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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; both teacher heads agree on what is shown here.
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".