Modeling Driver Take-Over Reaction Time and Emergency Response Time using an Integrated Cognitive Architecture
Bibliographic record
Abstract
Drivers’ take-over reaction time in partially automated vehicles is a fundamental component of automated vehicle design requirements, and take-over reaction time is affected by many factors such as distraction and drivers’ secondary tasks. This study built cognitive architecture models to simulate drivers’ take-over reaction time in different secondary task conditions. Models were built using the queueing network-adaptive control of thought rational (QN-ACTR) cognitive architecture. Drivers’ task-specific skills and knowledge were programmed as production rules. A driving simulator program was connected to the models to produce prediction of reaction time. Model results were compared with human results in both single-task and multi-task conditions. The models were built without adjusting any parameter to fit the human data. The models could produce simulation results of take-over reaction time similar to the human results in take-over conditions with visual or auditory concurrent tasks, as well as emergency response time in a manual driving condition. Overall, R square was 0.96, root mean square error (RMSE) was 0.5 s, and mean absolute percentage error (MAPE) was 9%. The models could produce simulation results of reaction time similar to the human results from different task conditions. The production rules are plausible representations of drivers’ strategies and skills. The models provide a useful tool for the evaluation of take-over alert design and the prediction of driver performance.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".