Identifying the Operational Design Domain for an Automated Driving System through Assessed Risk
Bibliographic record
Abstract
Assuring the safety of autonomous vehicles is one of the most significant challenges in the automotive industry. Tech companies and automotive manufacturers use the idea of Operational Design Domain (ODD) to indicate where their Automated Driving Systems (ADS) can operate safely. By definition from SAE J3016, an ODD defines where the ADS is designed to operate. However, it is loosely defined in no particular format, and it is unclear how exactly to formulate the ODD, which leaves it up to the ADS developer to determine. This paper proposes a methodology to identify an ODD for an ADS with statistical data and risk tolerance, where the identified ODD is constituted of a geographical map where the risk of ADS operation is lower than the pre-determined risk threshold for a given set of environmental conditions. Two different ADSs are run through this method as an example to showcase the methodology and link the identified ODD directly to the calculated performance of the ADSs. This systematically generated ODD can mitigate potential safety issues by informing the limitations of the ADS to safety drivers, through geographic and environmental boundaries.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".