Gaze-Based Indicators of Driver Cognitive Distraction: Effects of Different Traffic Conditions and Adaptive Cruise Control Use
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Summary
This study investigates how gaze parameters reflect driver cognitive distraction under varying traffic conditions and adaptive cruise control (ACC) use. Motivated by the need for reliable driver monitoring systems, the research addresses how external factors like traffic complexity and automation influence gaze-based indicators of mental workload. The authors conducted a driving simulator study with 29 participants across six scenarios combining three levels of driving environment complexity and two levels of cognitive distraction (intermittent mental calculations). Participants could freely activate or deactivate ACC. Using an infrared camera, the researchers analyzed percent road center and horizontal and vertical gaze dispersions during highway segments. Results showed that vertical gaze dispersion increased with traffic complexity, while ACC use led to greater gaze concentration toward the road center and reduced dispersion in both axes. Cognitive distraction significantly reduced percent road center and increased vertical dispersion. However, complementary analyses revealed that gaze actually concentrated toward the road center during mental calculations. The observed increase in dispersion stemmed from interleaving periods between calculations, suggesting drivers disperse their gaze to regain situational awareness after cognitive demand. These findings challenge prior literature linking cognitive distraction solely to gaze concentration. The study highlights that gaze patterns depend on the timing of cognitive tasks and the presence of automation. It implies that driver monitoring systems must account for dynamic gaze shifts and the specific nature of distracting tasks to accurately assess driver state.
Key finding
Vertical gaze dispersion increases with traffic complexity while ACC use leads to gaze concentration toward road center. Cognitive distraction reduces road center gaze and increases vertical dispersion, with effects arising mainly between mental calculations rather than during them.
Methodology
lab_experiment
Sample size: 29
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. Discovered via discover_arxiv on 2026-05-04 (4 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | arxiv | — | — | 3 | 2026-05-04 |
| archive | success | — | — | — | 1 | 2026-05-04 |
| extract | success | cached | — | — | 2 | 2026-06-07 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-04 |
| promote | success | — | — | — | 1 | 2026-05-04 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-07 |
| tag | success | vector_similarity | — | — | 17 | 2026-06-11 |
| verify | success | — | — | — | 1 | 2026-05-08 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-07; verification: verified.
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- Empirical Findings: behavioral performance data
- Methodological Resource: tool software
- Theoretical Contribution: conceptual framework