Visual exploration strategies of experienced and non-experienced drivers during the takeover of an automated vehicle
Bibliographic record
Abstract
Visual scanning of the driving environment is a key component in driving. In conditional automation, the vehicle is usually steered by automation, but drivers may be required to take control of the vehicle in certain circumstances. In these takeover situations (TO), visual exploration of the environment is critical for resuming manual control. This study aimed to compare visual explorations over time during TO preparation and actual TO between experienced and non-experienced drivers. Twenty-five participants completed three simulated drives, each comprising three TO maneuvers. Visual strategies were measured by the percentage of gaze time allocated to areas of interest (AOIs) and further characterised through visual exploration sequences (scanpaths) during TO preparation and actual TO. The results indicated that experienced drivers gazed more at the road area in which the vehicle would be located at the time of effective TO than non-experienced drivers. Similarly, immediately after resuming manual control, experienced drivers gazed more at areas where potential hazards may be present than non-experienced drivers. Three main scanpath classes were identified during TO preparation: anticipation of trajectory control, anticipation of speed control, and environment exploration. During subsequent manual driving, two scanpath classes emerged: speed control and trajectory control. These findings are discussed in terms of differences in visual strategies between non-experienced and experienced drivers, shaped by situational context. Functionally classifying visual exploration sequences provides insight into TO processes and offers a promising link to drivers’ situation awareness and performance.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".