Driver’s Attitudes Toward the Safety of In-Vehicle Navigation Systems
Bibliographic record
Abstract
There is anecdotal evidence of drivers blindly following in-vehicle navigation system (IVNS) commands. IVNSs have shown to be distracting and mishaps with the device have entered popular culture as a source of comedy. Manufactures have reacted by warning drivers of the dangers involved in operating the devices and in some cases prevent address input while moving. While IVNSs are increasingly being used, do drivers perceive their use as distracting, potentially misleading, and thus dangerous? We conducted an online survey of over 200 drivers to determine their attitudes toward safety while using these devices. This was followed by a series of interviews with an additional 20 drivers to provide more in-depth results. Drivers reported that distraction is not a big issue for them when using an IVNS, with only 8% reporting that the device was too distracting at times. Over 90% of respondents believe IVNSs do not have a harmful or potentially injurious effect and they are not wary of the device. They also placed more trust in directions from IVNSs than from people. There is a discrepancy between drives attitudes towards safety and potential dangers of using an IVNS. Drivers may be unaware of how distraction affects their driving. Some did not feel using an IVNS was dangerous at all because they are ultimately responsible for any incidents while driving.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".