Hazard Perception in Driving: A Systematic Literature Review
Bibliographic record
Abstract
Hazard perception (HP) is the process of detecting and identifying hazards. Drivers’ HP abilities are critical for driving safety. This paper presents a systematic literature review of driver HP, including scientific measures of HP, major human factors affecting HP, and training methods for improving HP skills. Sixty-nine peer-reviewed studies were identified and reviewed. The results showed that common measures of HP include hazard scenario questionnaires, HP reaction time, hazard hit rate, and eye fixation measures such as fixation probability, fixation reaction time, fixation duration, and fixation variance. Major human factors that affect HP include experience, aging, fatigue, distraction, and the use of alcohol and drugs. Various training methods have been developed to train and improve drivers’ HP skills. In general, there is evidence in the literature showing the effectiveness of HP training. A combination of complementary training approaches such as instruction, expert demonstration, and active practice with feedback and attention support the use of picture-, video-, computer-, and simulator-based training methods to improve HP performance in shorter HP reaction time, higher hazard hit rate, and better eye scan patterns (more spread scan, more anticipatory scan). These findings could guide future work developing and designing HP training programs. Three future research areas are identified and discussed: the need for standardized HP tests, long-term testing of HP training programs, and new HP questions and challenges brought by partially automated vehicles.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| 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".