Design and Automatic Generation of Safety Cases of ML-Enabled Autonomous Driving Systems
Bibliographic record
Abstract
Safety cases play a pivotal role in ensuring system reliability and acceptability, providing a structured argument supported by evidence. However, gaps in safety case literature hinder comprehensive safety assurance practices. In this thesis, we address this challenge through a three-fold approach. First, we conducted a bibliometric analysis following PRISMA 2020 guidelines to identify trends and knowledge gaps in safety assurance research. The analysis reveals critical areas lacking full safety cases and highlights the need for automated safety case construction. Then, we manually constructed a safety case for an ML-enabled component of an autonomous vehicle. Finally, leveraging large language models like GPT-4, we conducted experiments to automate safety case generation. Results indicate that GPT-4 produces safety cases with moderate accuracy and high semantic similarity to ground truth cases. This comprehensive methodology enhances safety practices, aiding researchers, analysts, and regulators in achieving robust safety assurance in complex systems.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".