How Do Drivers Self-Regulate their Secondary Task Engagements? The Effect of Driving Automation on Touchscreen Interactions and Glance Behavior
archive: archived pipeline: cataloged verified
Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)
Summary
This study investigates how drivers self-regulate their engagement with in-vehicle touchscreen interfaces under varying levels of driving automation, vehicle speed, and road curvature. Motivated by the increasing prevalence of large touchscreens and automated driving systems, the authors address a gap in existing research regarding tactical and operational self-regulation specifically during interactions with In-Vehicle Information Systems (IVIS). The research aims to determine if and how drivers adjust their interaction likelihood and glance behavior in response to these contextual factors, thereby informing the safety evaluation of infotainment systems and the development of context-aware driver monitoring. The researchers employed multilevel modeling on a real-world naturalistic driving dataset comprising 10,139 interaction sequences extracted from over 100 Mercedes-Benz test vehicles. The data included touchscreen interactions, driving metrics (speed, steering angle, automation level), and eye-tracking data collected via a stereo camera. The study analyzed three automation levels: manual (Level 0), Adaptive Cruise Control (ACC, Level 1), and ACC combined with Lane Centering Assist (LCA, Level 2). Independent variables included automation level, vehicle speed (categorized into three ranges), and road curvature (straight vs. curved). Dependent variables for tactical self-regulation were the probabilities of interacting with specific UI elements, while operational self-regulation was measured by mean glance duration and the likelihood of glances exceeding two seconds. The results indicate significant differences in driver behavior across automation levels. For tactical self-regulation, drivers were more likely to interact with certain UI elements during automated driving; for instance, the odds of interacting with map elements were 2.3 times higher during ACC+LCA driving compared to manual driving. Regarding operational self-regulation, mean glance durations increased by 12% during ACC driving and by 20% during ACC+LCA driving relative to manual driving. Furthermore, the odds of performing a glance longer than two seconds were 2.7 times higher for ACC and 3.6 times higher for ACC+LCA. The study also found that the effect of driving automation on self-regulation was more pronounced than the effects of vehicle speed or road curvature. These findings suggest that while drivers do self-regulate, they allow for longer and more frequent visual distractions when driving automation is active, potentially overestimating the safety margins provided by these systems. The authors conclude that this knowledge is critical for designing safer IVIS interfaces and for developing attention management systems that can detect inappropriate self-regulation. The study highlights the need for context-aware interventions, as the risk associated with long off-road glances increases significantly with higher levels of automation, independent of other driving demands.
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 | 2026-08-09 |
| archive | success | unpaywall | — | — | 2 | 2026-08-09 |
| extract | success | cached | — | — | 3 | 2026-08-10 |
| clean | success | clean | — | — | 1 | 2026-08-09 |
| chunk | success | chunk | — | — | 1 | 2026-08-09 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-08-09 |
| enrich | success | semantic_scholar | — | — | 1 | 2026-08-09 |
| promote | success | — | — | — | 1 | 2026-08-09 |
| summarize | success | llm | qwen3.6-27b-nvidia | summ-v5 | 2 | 2026-08-10 |
| tag | success | vector_similarity | — | — | 10 | 2026-08-11 |
| verify | success | — | — | — | 2 | 2026-08-10 |
Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
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, observational prevalence
- Theoretical Contribution: conceptual framework