Automated Driving System Disengagement Analysis and Testing Recommendations
Bibliographic record
Abstract
Abstract Automated Driving System refers to a vehicle system where hardware and software are collectively capable of on-road operational and tactical functions. Therefore, appropriate verification and validation procedures should be followed to mitigate unreliability and hazardousness. Autonomous Vehicle Disengagement Reports and Autonomous Vehicle Collision Reports from the Department of Motor Vehicles (DMV), California, USA are collectively used for the purpose of this research. Influencing factors are generated and consolidated from the mentioned reports and are proposed in addition to a Society of Automotive Engineers International (SAE International) standard. Stakeholders will benefit from the presented rationales and should consider the suggestive parameters throughout their developing and testing activities. This paper further recommends testing management, especially test driver management and test routes planning, for automated driving systems in accordance with the analytical results and feedback from KPMG’s Global Automotive Executive Surveys.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".