Driver arousal and workload under partial vehicle automation: A pilot study
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Summary
This pilot study investigates whether drivers’ cognitive arousal and workload differ between manual driving (Level-0) and semi-automated driving (Level-2) on real-world roads. The research addresses a critical safety gap: while Level-2 vehicles automate lateral and longitudinal control, they require continuous driver supervision. The study aimed to determine if the cognitive states of drivers using Level-2 automation are comparable to those in manual driving, a prerequisite for safe handover of control. The motivation stems from the need to understand if semi-automation leads to disengagement or boredom, which could increase accident vulnerability. The study employed a multi-method, within-subjects factorial design involving 28 participants who drove three different vehicles (Cadillac CT6, Tesla Model S, and Volvo XC90) on a 44-mile urban highway route. Participants had no prior experience with Level-2 automation. The design included two automation conditions (Level-0 vs. Level-2) and three vehicle types. Data collection utilized a mobile psychophysiological system to record continuous electrocardiography (ECG) and electroencephalography (EEG). Five outcome measures were analyzed: heart rate, root mean square of successive heart period differences (RMSSD), EEG alpha power, and hit rate and reaction time on a Detection Response Task (DRT). Statistical analysis involved linear mixed-effects models for conventional hypothesis testing and Bayes Factor analysis to interpret the strength of evidence for the null hypothesis. Results indicated no significant differences between Level-0 and Level-2 driving for any of the five cognitive arousal and workload measures. Specifically, mixed-effects models showed that automation did not significantly affect heart rate, RMSSD, EEG alpha power, DRT hit rate, or DRT reaction time. Vehicle type had a significant effect on DRT hit rate but did not interact with automation. Crucially, Bayes Factor analyses provided strong evidence favoring the null hypothesis for all five measures, with values ranging from 0.030 to 0.059. This statistical approach confirmed that the lack of observed differences was not merely due to insufficient power but represented a genuine absence of effect. The findings suggest that drivers new to semi-automated vehicles maintain cognitive arousal and workload levels comparable to manual driving. This implies that, for novice users, Level-2 automation does not inherently induce the low-arousal states (such as drowsiness or disengagement) that might compromise safety. The study contributes to the field by providing robust, multi-modal evidence that cognitive engagement remains stable during semi-automated driving in real-world conditions. Future research is recommended to examine drivers with higher levels of experience in automated vehicles, as their engagement levels may differ from those of the novice participants in this study.
Key finding
In drivers new to Level-2 automation, cognitive arousal and workload during partial automation are equivalent to manual driving. Bayes Factors of .030-.059 across heart rate, RMSSD, EEG alpha, DRT hit rate, and DRT reaction time provide strong evidence for the null, suggesting these drivers remained engaged with the driving task rather than becoming disengaged or overloaded.
Methodology
on_road
Sample size: N=28 (24% female; M_age=29.29, SD=4.27). Inclusion criteria: valid license, no at-fault crashes in past 2 years, >=10 hours/month driving, no neurological or heart conditions, no prior Level-2 experience.
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 author_sweep_intake on 2026-05-28 (5 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | author_sweep | — | — | 3 | 2026-05-28 |
| archive | failed | pmc | — | — | 8 | 2026-06-04 |
| extract | success | cached | — | — | 12 | 2026-08-22 |
| clean | success | clean | — | — | 2 | 2026-08-10 |
| chunk | success | chunk | — | — | 2 | 2026-08-10 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 2 | 2026-08-10 |
| enrich | success | — | — | — | 1 | 2026-05-06 |
| promote | success | — | — | — | 2 | 2026-06-06 |
| summarize | success | llm | qwen3.8-27b-gittensor | summ-v5 | 2 | 2026-08-22 |
| tag | success | vector_similarity | — | — | 27 | 2026-08-11 |
| verify | success | — | — | — | 1 | 2026-05-08 |
Summary generated by qwen3.8-27b-gittensor on 2026-08-22; verification: pending re-verification.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- workload measurement
- hands on hands off engagement
- drowsiness detection algorithms
- situational awareness
- mental demand
- stress driving
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: physiological data
- Theoretical Contribution: theory or model, conceptual framework