Applications of brain imaging methods in driving behaviour research
DOI: 10.1016/j.aap.2021.106093
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Summary
This review paper examines the application of neuroimaging methods in driving behavior research, aiming to deepen the scientific understanding of human factors in road safety. Motivated by the significant societal and economic costs of traffic accidents, where human error is a dominant cause, the study seeks to move beyond external behavioral observations to investigate the underlying neural and cognitive mechanisms of driving. The authors systematically review literature to identify prevalent imaging methods, synthesize findings across major research themes, and highlight gaps in current knowledge. The methodology involves a systematic search of databases including Web of Science, Scopus, and Google Scholar, utilizing specific keywords related to driving behavior and brain imaging. From an initial dataset of 357 documents, the authors filtered for peer-reviewed journal articles that provided behavioral insights via brain imaging data, resulting in a core dataset of 85 studies. The review focuses on four primary neuroimaging techniques: Functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG), Functional Near-Infrared Spectroscopy (fNIRS), and Magnetoencephalography (MEG). The analysis is conducted at both macro and micro scales, categorizing studies into seven themes: intoxicated driving, distracted driving, drivers with neurological impairments, semi-automated driving, fatigue/sleep deprivation, decision-making/risk-taking, and general driving behavior in healthy subjects. The findings reveal that fMRI and EEG are the most commonly used methods, each serving distinct research purposes. fMRI has been instrumental in studying the neural correlates of intoxicated driving (alcohol and cannabis) and distracted driving, often utilizing Independent Component Analysis to handle the complex, continuous nature of driving tasks. In contrast, EEG has seen a surge in application since 2014, predominantly for developing automatic fatigue and drowsiness detection systems. These studies frequently employ machine learning and deep learning algorithms to analyze EEG signals for early detection of mental fatigue. The review identifies that research has heavily focused on healthy, sober drivers, with limited investigation into drivers with brain injuries, chronic neurological conditions, or those operating in semi-automated settings. The significance of this work lies in its comprehensive synthesis of an interdisciplinary field, bridging transportation science, neuroscience, and psychology. By mapping the evolution and distribution of research topics, the paper highlights the potential of neuroimaging to predict driver mental states and decision-making intentions. It underscores the need for future research to explore underrepresented areas, such as the neuro-cognitive characteristics of drivers with neurological impairments and the brain activity associated with automated driving systems, thereby informing the design of safer vehicle control interfaces and road safety interventions.
Key finding
fMRI has been particularly instrumental in studying neural correlates of intoxicated and distracted driving, while EEG dominates fatigue/drowsiness detection research; topics such as neural correlates of semi-automated driving and drivers with chronic neurological conditions remain under-investigated.
Methodology
review
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 tag_papers on 2026-05-30 (3 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | — | — | — | 1 | 2026-05-07 |
| archive | success | canonical_url | — | — | 2 | 2026-06-03 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-07 |
| promote | success | — | — | — | 3 | 2026-06-06 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 18 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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- Empirical Findings: physiological data
- Methodological Resource: tool software
- Theoretical Contribution: computational model