Using an Integrated Cognitive Architecture to Model the Effect of Environmental Complexity on Drivers’ Situation Awareness
Bibliographic record
Abstract
The goal of this research is to computationally model and simulate drivers’ situation awareness (SA). In order to achieve this, we have developed a computational cognitive model in a cognitive architecture that can be connected to interact with a driving simulator, as means to infer quantitative predictions of drivers’ SA. We demonstrate the theory of modelling and predicting SA through the lens of human cognition utilizing the QN-ACTR (Queueing Network-Adaptive Control of Thought-Rational) framework as a foundation. We integrate a dynamic visual sampling model (SEEV) to create QN-ACTR-SA in order to allow the model to simulate realistic attention allocation patterns of human drivers. A driver model is also incorporated within QN-ACTR-SA architecture that can simulate human driving behavior by interacting with a driving simulator with the help of virtual modalities such as motor, visual and memory functions. A preliminary validation study is conducted to determine whether SA results of the model correspond to empirical data. The model is probed with SA queries similar to how a Situation Awareness Global Assessment Technique (SAGAT) is conducted on human participants. A comparative assessment demonstrates the model’s ability to simulate drivers’ SA in both easy (with fewer traffic vehicles and signboards) and complex (with more traffic vehicles and signboards) driving conditions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".