A Review of Driver Mental Workload in Driver-Vehicle-Environment System
DOI: 10.1007/978-3-642-21660-2_14
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Summary
This review paper examines the state of research regarding driver mental workload within the Driver-Vehicle-Environment (DVE) system. The authors aim to identify shortcomings in existing literature and outline opportunities for future study, addressing the lack of a universally accepted definition for mental workload and the tendency of previous reviews to focus heavily on methodology rather than the interrelated factors of driving. The study analyzes 50 of the most-cited journal papers retrieved from the ISI Web of Science database, classifying them into four categories: driver characteristics, vehicle characteristics, environmental characteristics, and measurement and modeling. The review synthesizes findings across these domains. Regarding driver characteristics, research highlights that visual scanning and information processing are critical capacities affected by workload. Older drivers tend to organize car-controlling movements serially to reduce momentary workload, and high workload significantly increases reaction times, particularly for elderly drivers. Vehicle characteristics, specifically In-Vehicle Information Systems (IVIS) and automation, present significant challenges. While Head-Up Displays (HUDs) induce less workload than Head-Down Displays, mobile phone use increases subjective workload, with conversation complexity being a more significant distractor than the device type. Automation, such as Adaptive Cruise Control, generally reduces workload but can cause overburden during system dysfunction or when drivers must retake control. Environmental factors, including road curvature, edge lines, and traffic density, directly influence visual demand and performance; for instance, sharper curves and increased heavy goods vehicle presence elevate mental demand and reduce safety margins. In terms of measurement and modeling, the paper notes that techniques have remained largely unchanged for decades, relying on subjective assessments, performance measures, and physiological indicators. The authors emphasize that no single measure captures all aspects of workload; instead, a battery of techniques provides the most sensitive assessment. Physiological measures, such as heart rate and saccadic peak velocity, are increasingly recommended for their ability to detect workload changes before performance decrements occur. Various numerical models, including neural networks and queuing networks, have been developed to simulate workload, though their accuracy requires improvement. The authors conclude that while the multidimensional nature of mental workload is widely accepted, the field suffers from conceptual ambiguity and dissociative measurement approaches. There is a lack of a unified theoretical framework to explain the interactions between physiological phenomena and workload. Future research must integrate driver, vehicle, and environmental factors as uniform inputs to human information processing to accurately measure, assess, and predict mental workload, thereby improving vehicle design and road safety.
Provenance
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| 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 | failed | — | — | — | 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 | partial | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified_with_issues.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- mental demand
- workload measurement
- road complexity
- stress driving
- situational awareness
- cognitive capacity variation
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
- Methodological Resource: metric or index
- Theoretical Contribution: theory or model