Seeing the truck, but missing the cyclist: effects of blur on duration thresholds for road hazard detection
Bibliographic record
Abstract
Drivers must respond promptly to a wide range of possible road hazards, from trucks veering into their lane to pedestrians stepping onto the road. While drivers' vision is tested at the point of licensure, visual function can degrade, and drivers may not notice how these changes impact their ability to notice and respond to events in the world in a timely fashion. To safely examine the potential consequences of visual degradation on hazard detection, we performed two experiments examining the impact of simulated optical blur on participants' viewing duration thresholds in a hazard detection task, as a proxy for eyes-on-road duration behind the wheel. Examining this question with older and younger participants, across two experiments, we found an overall increase in viewing duration thresholds under blurred conditions, such that younger and older adults were similarly impacted by blur. Critically, in both groups, we found that the increment in thresholds produced by blur was larger for non-vehicular road hazards (pedestrians, cyclists and animals) compared to vehicular road hazards (cars, trucks and buses). This work suggests that blur poses a particular problem for drivers detecting non-vehicular road users, a population considerably more vulnerable in a collision than vehicular road users. These results also highlight the importance of taking into account the type of hazard when considering the impacts of blur on road hazard detection.
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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 | 0.001 | 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 | 0.001 | 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) | 0.000 | 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".