Automating driver visual behavior measurement
Bibliographic record
Abstract
This thesis is devoted to understanding and counteracting the primary contributing factor in traffic crashes: inattention. Foremost, it demonstrates the fundamental importance of proactive gaze in the road centre area for action guidance in driving. Inattention is explained with regard to two visual functions (vision-for-action and vision-for-identification), three forms of attentional selection (action-driven-, stimulus-driven-, and goal-directed attention), and two forms of prediction influences (extrapolation-based- and decision-based prediction influences). In Study I an automated eye-movement analysis method was developed for a purpose-built eye-tracking sensor, and was successfully validated. This analysis method was further developed, and several new measures of gaze concentration to the road centre area were created. Study II demonstrated that a sharp decrease in the amount of road centre viewing time is accompanied by a dramatic spatial concentration towards the road centre area in returning gaze during visual tasks. During cognitive tasks, a spatial gaze concentration to road centre is also evident; however contrary to visual tasks, road centre viewing time is increased because the eyes are not directed towards an object within the vehicle. Study III found that gaze concentration measures are highly sensitive to driving task demands as well as to visual and auditory in-vehicle tasks. Gaze concentration to the road centre area was found as driving task complexity increased, as shown in differences between rural curved- and straight sections, between rural and motorway road types, and between simulator and field motorways. Further, when task duration was held constant and the in-vehicle visual task became more difficult, drivers looked less at the road centre area ahead, and looked at the display more often, for longer periods, and for more varied durations. In closing, it is shown how this knowledge can be applied to create in-vehicle attention support functions that counteract the effects of inattention.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".