Gamification of Driver Distraction Feedback: A Simulator Study With Younger Drivers
Bibliographic record
Abstract
Providing personalized behavioral information as feedback to drivers can lead to safer practices. However, feedback efficacy is likely moderated by the driver's level of motivation towards behavioral change. Gamification of feedback, which is the incorporation of game design elements intended to motivate drivers toward safe behaviors, could potentially reduce unsafe behaviors in the long term. This article assesses a gamified driver feedback design in mitigating driver distraction and enhancing driving performance among younger drivers. A driving simulator study was conducted with 42 drivers, 21–30 years old, comparing: 1) no feedback; 2) real-time feedback; 3) real-time feedback + postdrive feedback; and 4) real-time feedback + postdrive feedback + game design elements to examine their impact on distraction engagement (manual-visual interactions with an in-vehicle display) and driving performance. Groups that received postdrive feedback, both with and without gamification elements, showed reduced distraction engagement and enhanced driving performance compared to no feedback. Between the two types of postdrive feedback, the nongamified feedback provided more benefits in reducing the 95th percentile glance duration to the in-vehicle display, and the one with gamification provided more benefits in reducing the rate of manual interactions with the in-vehicle display. Meanwhile, no benefits were observed with the real-time feedback only condition over no feedback. Despite minor differences in efficacy, both postdrive and gamification feedback appear to be effective countermeasures for distracted driving in the short term. Future research should investigate other game designs for driver feedback and assess the impact of feedback gamification over longer-term exposure.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".