Proposal of a new virtual evaluation approach of preventive safety applications and advanced driver assistance functions – application: AEB system
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
This study presents a new virtual evaluation approach of preventive safety applications and advanced driver assistance functions. The approach identifies the worst‐case scenarios for a given advanced driver assistance function, AEB system in this study, based on field operational tests (FOT) [safety pilot model deployment (SPMD), in this study]. The authors begin with a description of the studied AEB system and a synthesis of the most relevant tests scenarios. Then, they model the distribution of each test parameter retrieved from the SPMD database by applying two estimation methods (kernel method and expectation‐maximisation algorithm). A comparison was made between the two methods to choose the best one. These distributions are then sampled using the proposed sampling strategy based on Metropolis‐Hastings algorithm. Then, the idea is to take the samples of each parameter retrieved with this sampler, simulate them on a vehicular software simulator (PreScan) and to get their simulation results. For each test and in case of impact, a proportional score to the speed of impact reduction is attributed. Finally, a risk classification is done based on the scoring results which allows to recover high and very high‐risk cases to build a set of worst‐case scenarios.
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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 | 0.000 |
| 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