Applying the Operational Design Domain Concept to Vehicles Equipped with Advanced Driver Assistance Systems for Enhanced Safety
Bibliographic record
Abstract
Advanced driver assistance systems (ADASs) assist drivers by alerting them of the occurrence of events based on the sensing capabilities of the vehicle, reducing the effort required by drivers. Most vehicles that are recently launched vehicles have been endowed with ADAS, thereby significantly reducing traffic accidents. However, the Insurance Institute for Highway Safety has reported that traffic accidents caused by driver negligence may increase as drivers have become accustomed to using ADAS. Therefore, drivers must be provided with sufficient information on the appropriate use of ADAS through user manuals. In this study, the regulations regarding the presentation of the operational design domain (ODD) in ADAS user manuals were analyzed. The results indicated that most user manuals do not sufficiently specify the ODD, which is claimed important by various organizations for ensuring safe driving. Additionally, the expression of the limitations and performance of ADAS is ambiguous because most countries are not regulated to explicitly present the ODD when writing ADAS user manuals. Therefore, in this study, the ODD guidelines for presenting ADAS specific to vehicle manufacturers and governments have been outlined in addition to guidelines for drivers on using ADAS. These guidelines can contribute to the development of clear ADAS user manuals, which in turn can ensure the safe driving of ADAS-equipped vehicles.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".