Modeling Brake Perception Response Time in On-Road and Roadside Hazards Using an Integrated Cognitive Architecture
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
In this article, we used a computational cognitive architecture called queuing network–adaptive control of thought rational–situation awareness (QN–ACTR–SA) to model and simulate the brake perception response time (BPRT) to visual roadway hazards. The model incorporates an integrated driver model to simulate human driving behavior and uses a dynamic visual sampling model to simulate how drivers allocate their attention. We validated the model by comparing its results to empirical data from human participants who encountered on-road and roadside hazards in a simulated driving environment. The results showed that BPRT was shorter for on-road hazards compared to roadside hazards and that the overall model fitness had a mean absolute percentage error of 9.4% and a root mean squared error of 0.13 s. The modeling results demonstrated that QN–ACTR–SA could effectively simulate BPRT to both on-road and roadside hazards and capture the difference between the two contrasting conditions.
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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 | 0.001 | 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 | 0.001 |
| 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