In-Vehicle Interface Adaptation to Environment-Induced Cognitive Workload

Meiser, Elena; Alles, Alexandra; Selter, Samuel; Molz, Marco; Gomaa, Amr; Reyes, Guillermo · 2022 · arXiv

DOI: 10.1145/3544999.3552533

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Summary

This paper addresses the problem of cognitive distraction in driving, which contributes significantly to traffic accidents. As in-vehicle human-machine interfaces (HMIs) become more feature-rich, they risk increasing driver mental workload (MWL) rather than aiding it. The authors propose adaptive HMIs that simplify their display based on environmental complexity to reduce MWL. The study investigates whether such adaptation effectively lowers cognitive load and improves user experience compared to static interfaces. The researchers conducted a user study with 35 participants using a medium-fidelity driving simulator. Participants were divided into two groups: one using a static interface and the other an adaptive interface that simplified its display when transitioning from a low-complexity countryside environment to a high-complexity city environment. Participants performed secondary tasks of varying difficulty (easy, medium, hard) while driving. Mental workload was measured physiologically via heart rate (HR) and heart rate variability (HRV) using chest-worn sensors. Behavioral metrics included driving performance, task completion latency, and success rates. User experience (UX) was assessed via a post-study questionnaire. The preliminary results contradicted the study’s hypotheses. Instead of increased workload in the complex city environment, participants showed a trend toward decreased MWL, suggesting a training effect or compensatory speed reduction rather than environmental overload. Contrary to expectations, the adaptive interface did not reduce MWL or improve performance; participants in the static group demonstrated better secondary task performance (fewer clicks) when moving to the city environment. Task difficulty significantly affected performance metrics, but the adaptive interface did not mitigate these effects. Furthermore, UX ratings showed no significant difference between the static and adaptive conditions, indicating that drivers did not perceive the adaptive interface as superior. The findings suggest that adapting HMI information density based on environmental complexity may not reduce mental workload and could potentially confuse drivers, hindering performance. The authors attribute the unexpected results to potential learning effects, insufficient environmental difficulty manipulation, and compensatory driving strategies like speed reduction, particularly among experienced drivers. The study highlights the need for standardized methods for manipulating driving environments and suggests that future research should focus on long-term HMI interaction and alternative adaptation strategies to ensure safety and usability.

Key finding

Contrary to predictions, the adaptive HMI did not reduce MWL relative to the static HMI: the static group actually showed a smaller increase in clicks and a larger learning effect from countryside to city, HR decreased (rather than increased) in the city condition suggesting an ineffective workload manipulation or compensatory speed reduction, and UEQ+ ratings did not differ between groups. The authors conclude that interface adaptation can confuse drivers and that more careful manipulation of environmental difficulty plus driving-experience controls are needed.

Methodology

simulator

Sample size: 35 participants; adaptive group n=16 vs static group n=19 (between-subjects). Static group reported significantly higher annual driving km (M=10639, SD=4259) than adaptive group (M=2733, SD=12835), t(33)=2.35, p=.025.

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 2026-05-07 (4 acquisition events logged).

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.

• mental demand
• road complexity
• workload measurement
• automation
• dual task performance

Information type

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• Applied Guidance: design guidelines
• Empirical Findings: physiological data
• Theoretical Contribution: theory or model