In-vehicle displays for driving automation: a scoping review of display design and evaluation using driver gaze measures
Bibliographic record
Abstract
Recent research has extensively examined in-vehicle display designs for supporting the operation of driving automation. As automation relieves drivers from various driving tasks including vehicle control (e.g. steering, accelerating, and braking), driving performance measures (e.g. speed, lane deviations) may not be informative indicators for evaluating the effectiveness of in-vehicle displays. Gaze-based measures are a better alternative given their link to driver visual attention, an indication of driver engagement. A scoping review was conducted to review the literature on the design of displays for supporting the operation of driving automation and the evaluation of these displays using gaze-based measures. Forty-three articles were included in the review. Most of the studies investigated visual (and mixed visual-auditory) displays that provide alerts to the driver for when to intervene automation classified as Level 3. The adopted gaze measures mostly relied on static areas of interest (AOIs), with fewer studies looking at more fine-grained, context dependent AOIs. The paper summarises the findings of the review, including research trends and gaps, as well as recommendations for future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".