Effectiveness of a time to fixate for fitness to drive evaluation in neurological patients
DOI: 10.3758/s13428-023-02177-3
archive: archived pipeline: cataloged verified
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Summary
This study addresses the challenge of evaluating fitness to drive in neurological patients, a process currently hampered by inconsistent guidelines and the lack of reliable, evidence-based metrics. The authors propose a novel method to automatically calculate "time to fixate" (TTF)—the interval between the appearance of a hazard and the driver’s first gaze fixation on it—using eye-tracking data and computer vision. The research aims to determine if TTF can effectively distinguish between patients classified as fit-to-drive, conditionally-fit-to-drive, and unfit-to-drive, and to compare this metric against the standard perception response time (PRT). The study utilized retrospective data from 108 neurological patients who underwent driving simulator assessments for license revalidation. After excluding subjects due to data quality issues or missing information, TTF was analyzed for 56 patients. The methodology involved processing video recordings from Tobii Pro Glasses 2 eye-trackers using the YOLO v5 object detector to identify pedestrians (children crossing the street) and cross-reference their location with the driver’s gaze coordinates. TTF was calculated as the time difference between the pedestrian’s appearance and the driver’s first fixation. The analysis also incorporated covariates including speed at hazard onset, initial gaze distance (IGD), and time-to-collision (TTC). Statistical comparisons were performed using Welch’s ANOVA and Tukey’s honest significant difference post hoc tests, with PRT serving as a ground truth comparison metric. The results demonstrated that the automated TTF calculation method was efficient and yielded discriminative power. Welch’s ANOVA indicated significant differences in TTF among the three patient groups ($p = 0.000$). Tukey’s post hoc tests revealed that fit-to-drive patients had significantly shorter TTFs compared to both unfit ($p = 0.001$) and conditionally-fit ($p = 0.0001$) patients. However, no significant difference was observed between the conditionally-fit and unfit groups ($p = 0.542$). The study found that speed, IGD, and TTC did not independently influence TTF results, though a significant interaction existed among fitness status, IGD, and TTC. Additionally, TTF showed a correlation with PRT, validating its relevance as a cognitive metric. The significance of this work lies in providing a validated, automated workflow for assessing visual attention and cognitive processing in neurological patients using driving simulators. By establishing TTF as a parameter that can distinguish fit drivers from those with impairments, the study offers a potential tool for medical professionals to make more objective decisions regarding driving privileges. The authors also highlight methodological challenges, such as eye-tracker slippage and object detection errors, providing detailed guidelines for future research in psychology, neuroscience, and traffic safety to ensure reliable data collection and analysis.
Key finding
Time-to-fixate metrics derived from eye-tracking data significantly distinguish fit-to-drive neurological patients from unfit and conditionally fit groups, offering a viable automated method for driving fitness evaluation.
Methodology
simulator
Sample size: 56
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-06 |
| archive | success | canonical_url | — | — | 1 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-07 |
| chunk | success | chunk | — | — | 1 | 2026-06-07 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-07 |
| promote | success | — | — | — | 1 | 2026-06-06 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- useful field of view
- hazard perception
- fitness to drive assessment
- post concussion
- looked but failed to see
- eye movements scanning
Information type
What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).
- Empirical Findings: behavioral performance data
- Methodological Resource: tool software, validation psychometrics