Design of the Safety Case of the Reinforcement Learning-Enabled Component of a Quanser Autonomous Vehicle
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Safety assurance is paramount across industries where mission-critical systems operate, mitigating risks of catas-trophic failures. Safety cases play a pivotal role, particularly in safety critical systems (e.g., autonomous vehicles), in ensuring system reliability and acceptability, providing a structured argument supported by evidence. However, in the safety case literature, it is challenging to get access to a complete safety case, which is crucial for the research community to contribute in this domain. Hence, in this research, we propose an approach to create a safety case for ML-enabled autonomous vehicle, specifically, the Quanser Qcar. We present a complete safety case for a reinforcement learning algorithm applied on the Quanser Qcar to avoid collisions in an unsignalized 4-way intersection. Finally, we report the lessons learned and provide the safety case for the research community to reuse.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it