REAL-SAP: Real-Time Evidence Aware Liable Safety Assessment for Perception in Autonomous Driving
Bibliographic record
Abstract
Self-evaluation and monitoring are critical components in autonomous driving applications, especially for safety purposes, and yet there is no systematic framework to estimate the learning-based perception system in real-time. This paper aims to provide a general strategy for estimating how reliable the perception results generated from the black-box neural networks are in real-time. The perception safety evaluation problem has been formulated in a probabilistic framework, and theoretical analysis suggests that the existing geofencing or rule-based safety checking is a simplified version of the proposed strategy. The offline testing knowledge and real-time measured evidence are encoded as conditional probabilities and priors in the Bayesian network. The confidence score of the neural networks is utilized as an auxiliary factor to regularize the perception safety evaluation. Simulation and experimental results demonstrate the effectiveness of the proposed safety evaluation for perception systems under virtual and real data in city driving. In addition, we show that the proposed method can generate the necessary warning signals to support downstream safety monitoring and fail-degraded system functionalities.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".