Automotive safety verification under temporal failure of adaptive cruise control system using statistical model checking
Bibliographic record
Abstract
Recently, a significant progress has been achieved in developing advanced driver assistant systems that improve the driver's comfort and safety. Adaptive Cruise Control (ACC) system is an example of these assistant systems which automatically controls the throttle and braking systems to maintain a safe distance between vehicles. Existing safety verification techniques for these systems consider the operation in normal conditions without taking the failure in sub-components into account for the safety assessment. In this work, we introduce an automotive safety analysis to investigate the impact of the temporal failures of the adaptive cruise control system. New probabilistic model of the ACC system is proposed based on Priced Timed Automata (PTA). This model is verified using statistical model checking technique. In this analysis, two temporal failure scenarios are considered: i) missing-control scenario, where neither throttle nor braking system receive control signals; ii) error-control scenario, where erroneous control signals due to failure in the distance sensors are introduced. The modeling and safety analysis of ACC system are fully automated by utilizing UPPAAL SMC. The analysis shows that the error-control failures have a worse impact on the automotive safety comparing with missing-control failures. In this work introduce a new insight to the automotive safety analysis under advanced driver assistant systems.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".