# Monitoring Technologies for Truck Drivers: A Systematic Review of Safety and Driving Behavior
## 3 Results
### Characteristics of Studies  
The temporal distribution of studies illustrates a consistent increase in research activity over the 15-year period analyzed (see Figure 2). By 2009, two studies had been published, increasing incrementally to three by 2012 and five by 2015. A notable acceleration occurred from 2018 onward, with the cumulative number of studies reaching 8 by 2018, 25 by 2021, and 40 by 2024. This upward trend underscores a growing emphasis on leveraging monitoring technologies to enhance truck driver safety.  
Geographically, the distribution of research remains uneven across the globe (see Figure 3). The majority of studies were conducted in the United States (35%) and China (20%), followed by Japan (8%) and Sweden (7%). Germany accounted for 5% of the studies, while 25% originated from various other countries. This pattern highlights a concentration of research in developed regions with advanced transportation systems. In contrast, regions with high accident rates, such as Sub-Saharan Africa and parts of South America [1], are underrepresented in the literature.  
The reviewed studies employed a variety of methodologies and technologies to address critical aspects of truck driver monitoring and safety. A significant portion of the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Quartile Ranking** & **Number of Studies** \\ \hline Q1 & 30 \\ \hline Q2 & 8 \\ \hline Q3 & 1 \\ \hline Q4 & 0 \\ \hline Not assigned & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of selected studies per quartile.  
Figure 3: Country-wise distribution of selected studies.  
Figure 2: Cumulative number of published studies on monitoring technologies for truck driver behavior assessment.  
research focused on advanced systems such as Camera/Video Imaging Systems (C/VISs), which were designed to mitigate blind spot risks and improve driver visibility. These systems underwent real-world testing to evaluate their effectiveness in preventing safety-critical events, such as during lane changes.  
Fatigue monitoring emerged as another prominent theme, with several studies exploring non-intrusive methods utilizing wearable devices, physiological data, and machine learning algorithms. These approaches aimed to detect driver fatigue in real-time without disrupting driving tasks. Multi-feature fusion techniques were frequently employed to integrate various data types, enhancing the precision and reliability of fatigue detection.  
Forward collision warning (FCW) systems were extensively studied, with considerable efforts directed toward optimizing their algorithms to better align with driver response behaviors. Machine learning models were commonly used to improve warning accuracy and reduce false alarms, addressing key challenges associated with FCW system adoption and reliability.  
Additionally, many studies leveraged GPS and telemetric data to analyze driver behavior. These datasets allowed researchers to identify risky driving patterns, such as erratic speed variations, and to establish risk profiles for truck drivers. Such analyses were often applied in fleet management strategies to monitor and mitigate on-road risks effectively.  
The methodologies utilized in these studies were diverse, encompassing real-world field testing, simulation-based experiments, and the integration of wearable and in-vehicle technologies. Advanced systems such as telematics, connected vehicle platforms, and artificial intelligence were frequently employed to assess and enhance truck driver safety across varied operational scenarios.  
This body of research represents a broad and methodical investigation into monitoring technologies within the trucking industry. The studies collectively highlight the range of innovative approaches used to address safety challenges, emphasizing the importance of advanced systems, data-driven methods, and interdisciplinary collaboration.