Monitoring Technologies for Truck Drivers: A Systematic Review of Safety and Driving Behavior
DOI: 10.3390/app15126513
archive: archived pipeline: cataloged verified
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Summary
This systematic review addresses the critical safety risks faced by professional truck drivers, who are disproportionately vulnerable to accidents due to fatigue, distraction, and demanding working conditions. Motivated by the fact that human behavior contributes to up to 90% of road accidents, the study evaluates how monitoring technologies can mitigate these risks. The authors aim to consolidate existing evidence on the effectiveness, benefits, and limitations of safety monitoring systems, specifically answering four research questions regarding the types of technologies employed, the variables tracked, their impact on safety outcomes, and their primary strengths and limitations. The study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. The authors performed systematic searches across PubMed, Scopus, Web of Science, and IEEE Xplore for peer-reviewed articles published in English between 2009 and 2024. Eligibility criteria required studies to focus on professional truck drivers and evaluate monitoring technologies such as wearable devices, in-vehicle cameras, telematics systems, and AI-driven platforms. After screening 725 records and excluding duplicates and irrelevant studies, 40 articles were included in the final synthesis. Due to significant methodological heterogeneity among the included studies, a formal risk of bias assessment was not conducted. Data extraction was performed using a standardized protocol, with assistance from AI tools for organization, followed by manual verification. The review found that the majority of included studies were published in high-impact journals, with a notable acceleration in research activity from 2018 onward. The technologies examined primarily monitored physiological, behavioral, and environmental variables, including fatigue, stress, distraction, speed, and driving patterns. While the evidence demonstrates considerable potential for these technologies to enhance safety outcomes through real-time feedback and post-event analysis, persistent challenges remain. These include high implementation costs, technical complexities, and significant concerns regarding data privacy and security. Furthermore, the evidence base is geographically concentrated in high-income regions, which limits the broader applicability of the findings. The significance of this review lies in its identification of the urgent need for harmonized evaluation frameworks and robust validation protocols to support the effective adoption of monitoring technologies in the trucking sector. The authors conclude that while traditional safety interventions have had limited success, technology-driven monitoring offers a viable alternative for continuous risk mitigation. However, to realize their full potential, stakeholders must address barriers related to cost, privacy, and effectiveness variability. The review provides actionable insights for researchers, policymakers, and practitioners to develop context-sensitive strategies that improve road safety and operational efficiency for commercial fleets.
Key finding
Monitoring technologies demonstrate significant potential to improve truck driver safety by tracking behavioral and physiological variables, but their implementation is constrained by cost, privacy issues, and a lack of standardized evaluation frameworks.
Methodology
review
Sample size: 40
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 author_sweep_intake on 2026-05-28.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | author_sweep | — | — | 2 | 2026-05-28 |
| archive | success | openalex | — | — | 9 | 2026-06-06 |
| extract | success | cached | — | — | 3 | 2026-06-10 |
| clean | success | clean | — | — | 1 | 2026-06-04 |
| chunk | success | chunk | — | — | 1 | 2026-06-04 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-04 |
| enrich | success | — | — | — | 1 | 2026-05-28 |
| promote | success | — | — | — | 1 | 2026-06-04 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 2 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 15 | 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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Information type
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- Empirical Findings: observational prevalence
- Methodological Resource: validation psychometrics, tool software