The influence of alcohol and automation on drivers visual behavior during test track driving
DOI: 10.1016/j.trf.2023.04.008
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Summary
This study investigates how alcohol intoxication affects drivers’ visual behavior across manual, assisted, and autonomous driving modes, aiming to determine if Driver Monitoring Systems (DMS) can reliably detect impairment. The research addresses the need for detection methods that function in all driving modes and capture impairment from various sources, not just alcohol. As operational control metrics become less viable in automated driving, visual attention metrics offer a promising alternative for identifying impaired states. The experiment utilized a mixed-design test track study involving 26 participants who performed two drives: a sober baseline and an intoxicated drive with a target blood alcohol concentration (BAC) of 0.1%. Participants were assigned to manual (SAE Level 0), assisted (SAE Level 2), or autonomous (SAE Level 3–4) driving modes. During the drives, participants performed non-driving related tasks (NDRTs), including tuning the radio, dialing a phone, and adjusting temperature settings. Visual behavior was recorded via eye-tracking and analyzed using metrics such as percent road center (PRC), off-path glance frequency, total glance time, and maximum off-path glance duration. Statistical analyses included Wilcoxon signed-rank tests for intoxication effects and Kruskal-Wallis tests for mode differences. Results indicated that intoxication significantly altered glance behavior in all driving modes. In manual mode without tasks, intoxicated drivers exhibited increased gaze concentration on the forward roadway, with nearly one-third showing PRC values above 92%. When performing visually demanding NDRTs, intoxication led to longer single and total off-path glance durations, particularly during radio tuning and dialing tasks. Driving mode also heavily influenced visual behavior; while differences between manual and assisted modes were moderate, autonomous driving resulted in a substantial shift toward longer off-path glances, lower PRC, and reduced off-path glance frequency compared to manual driving. These effects were consistent across both sober and intoxicated conditions. The study concludes that while intoxication clearly impacts visual behavior, reliable detection of impaired driving requires accounting for both the driving mode and engagement in NDRTs. Glance metrics, such as PRC and off-path glance duration, have potential as indicators for broader DMS-based impairment detection. However, because automation itself drastically changes visual patterns, DMS algorithms must be calibrated to distinguish between impairment-induced deviations and mode-specific behavioral shifts to effectively trigger in-vehicle countermeasures.
Key finding
Alcohol intoxication alters visual behavior by increasing gaze concentration during manual driving and prolonging off-path glances during non-driving tasks, with autonomous driving further amplifying off-road gaze durations.
Methodology
on_road
Sample size: 26
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 scout_discovery on 2026-05-08.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | partial | scout | — | — | 2 | 2026-05-08 |
| archive | success | openalex | — | — | 4 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | success | — | — | — | 1 | 2026-05-08 |
| promote | success | — | — | — | 1 | 2026-05-08 |
| 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.
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- Empirical Findings: behavioral performance data
- Methodological Resource: measurement protocol, tool software