Eye movements reveal that drivers can predict the location of hazards in dynamic road scenes but gaze and awareness are dissociable
Bibliographic record
Abstract
Parsing complex dynamic scenes is critical for navigating our visual world, and driving safely is a daily task that requires responding quickly to hazardous events. Theories of driver awareness suggest that drivers need to look at hazards to detect them, particularly in anticipation of hazard onset. Indeed, scene context may guide eye movements to likely hazard locations. However, given the complexity of real road scenes, drivers may rely instead on explicit cues of an impending collision before identifying the correct location. In 2024, we recorded eye position while 30 licensed drivers localized hazards in annotated dashcam footage. On correct trials, drivers started to look at where the hazard will be 2 s before onset, highlighting the importance of anticipatory processes in dynamic scene perception and safe driving. However, hazard-directed looking is not indicative of awareness, as 40% of missed hazards were foveated before response. Our results demonstrate early gaze guidance by scene context, which can help drivers anticipate hazards, an idea consistent with theories of driver awareness and with theories of visual search. However, looking directly at the hazard alone is not necessary or sufficient for hazard awareness and should not be used to index awareness in driver monitoring systems. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".