A Software QA Framework for Autonomous Vehicle Open Source Application: OpenPilot
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
As the deployment of autonomous vehicles (AV) grows, ensuring their reliability and safety becomes paramount. The need for rigorous testing and validation methods is surging. This paper focuses on investigating the resilience of OpenPilot, an open-source driving agent for assisted driving systems, against faults and environmental conditions affecting sensor data, targeting faults that directly impact machine learning (ML) and perception systems, known to be a significant cause of disengagement incidents in AV. To assess the effectiveness and coverage of Open-Pilot's functionalities, we employ standard model-driven test engineering methodology graph coverage testing. A systematic and comprehensive modeling of OpenPilot using graph coverage methodology is proposed, covering three programming scripts and yielding 16 major test case scenarios. This approach enables graph coverage testing on OpenPilot, facilitating evaluation of its performance, robustness, and safety measures. By systematically exploring the system's functionalities and scenarios, we ensure OpenPilot performs as expected and effectively mitigates safety hazards, contributing to the enhancement of autonomous vehicle safety and building confidence in autonomous driving technology.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.003 | 0.001 |
| 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