Mental workload and driving
archive: archived pipeline: cataloged verified
Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)
Summary
This review article examines the relationship between mental workload, driving performance, and driver characteristics, specifically focusing on situation complexity and driving experience. The authors aim to identify the most representative subjective and objective measures of mental workload and determine how these factors influence driving performance. The study is motivated by the need to understand the cognitive mechanisms behind road accidents, which are frequently attributed to human error and workload-related issues. The review provides guidance for researchers designing experimental studies and for developers of driver training programs. The paper employs a theoretical and empirical review approach, synthesizing existing literature on driving psychology. It categorizes driving tasks into strategic, tactical, and operational levels, linking them to controlled (conscious, effortful) and automatic (unconscious, routine) information processing. The authors evaluate subjective workload measures, comparing questionnaires such as the Subjective Workload Assessment Technique (SWAT), Workload Profile (WP), NASA Task Load Index (NASA-TLX), Rating Scale Mental Effort (RSME), and Driving Activity Load Index (DALI) against criteria like sensitivity, diagnosticity, and intrusiveness. Additionally, the review analyzes physiological indicators, including heart rate (HR), heart rate variability (HRV), and electroencephalogram (EEG) metrics, assessing their correlation with mental workload. Key findings indicate that mental workload follows an inverted U-shape relative to situation complexity; both monotonous (low complexity) and highly complex (high complexity) situations can lead to overload and performance impairments, whereas moderately complex situations allow for compensatory strategies. Driving experience significantly modulates these effects: novice drivers exhibit higher mental workload due to a lack of task automation and often underestimate situation complexity, leading to higher accident risks. Experienced drivers utilize automatic processing in simple situations and flexible, controlled processing in complex ones, allowing them to maintain better performance. Regarding measurement tools, the WP and NASA-TLX questionnaires are identified as having more advantages than others, though no single tool perfectly meets all criteria. Physiologically, HR and HRV are sensitive to workload but lack specificity, while EEG indicators like alpha/theta band changes and P300 amplitude offer high diagnosticity for cognitive processing but suffer from signal-to-noise issues. The significance of this work lies in its comprehensive comparison of subjective and objective workload measures, highlighting that neither alone fully captures the complexity of driving demands. The authors conclude that mental workload is a critical mediator between situation complexity, driver experience, and performance. Understanding these dynamics is essential for improving driver training, particularly for novices who struggle with workload management and risk assessment, and for designing safer driving environments and interfaces that account for cognitive limitations.
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 | 2026-06-25 |
| archive | success | canonical_url | — | — | 1 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-26 |
| chunk | success | chunk | — | — | 1 | 2026-06-26 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-26 |
| enrich | success | openalex | — | — | 1 | 2026-06-26 |
| promote | success | — | — | — | 1 | 2026-06-25 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-26 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- mental demand
- workload measurement
- stress driving
- road complexity
- cognitive capacity variation
- situational awareness
- time perception
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, self report data
- Theoretical Contribution: theory or model