# Monitoring Technologies for Truck Drivers: A Systematic Review of Safety and Driving Behavior
## 3 Results
### Monitoring Technologies
#### 3.3.3 Vision-Based Monitoring Systems  
Vision-based monitoring systems have emerged as a critical component in monitoring truck driver behavior and enhancing road safety. These systems employ advanced imaging technologies to observe driver activities and the surrounding environment, providing real-time feedback to mitigate risk factors such as fatigue, distraction, and blind spot-related crashes.  
Camera/Video Imaging Systems (C/VISs) are among the most commonly used vision-based tools in commercial vehicles. These systems provide live video feeds of a truck's surroundings, allowing drivers to monitor blind spots and make safer lane changes or merging maneuvers. Studies have demonstrated that C/VISs can improve situational awareness, particularly in challenging conditions such as nighttime driving and during complex tasks like right lane changes [26]. However, despite their potential, field evaluations have reported no significant reduction in safety-critical events (SCEs). Additionally, user feedback highlights discomfort with glare from monitors, particularly at night, and suggests that advanced features, while effective, are underutilized without sufficient driver acceptance and training [26].  
Another critical application of vision-based systems is fatigue detection. These systems typically rely on facial metrics, such as eyelid closure and eye movement patterns, to identify signs of drowsiness. While effective under controlled conditions, their performance diminishes in real-world scenarios due to challenges such as low-light environments, sensor limitations, and driver resistance to intrusive monitoring [27]. To address these issues, alternative approaches leveraging non-visual features, such as physiological and vehicular data, have shown promise. These systems utilize metrics like heart rate, skin conductance,and vehicle operation patterns, offering a privacy-preserving and robust solution for continuous fatigue monitoring. Comparative studies suggest that non-visual methods outperform traditional vision-based systems in terms of reliability and user acceptance, particularly in environments where visual cues are compromised [28].  
Driver monitoring systems (DMSs) extend the capabilities of vision-based systems by issuing real-time alerts for distraction and drowsiness. These systems typically employ auditory and visual alerts to prompt drivers to regain focus. Field studies have demonstrated an increase in driver alertness following such interventions, but their long-term effectiveness relies heavily on user engagement and trust [29; 30]. High rates of false alarms, coupled with poorly designed feedback mechanisms, can lead to driver frustration and disengagement, undermining the system's intended safety benefits [30; 31].  
Across the various vision-based technologies, several critical observations emerge. These systems demonstrate significant potential in enhancing road safety by reducing blind-spot-related crashes and improving situational awareness. However, their widespread adoption is hindered by high costs, operational complexity, and privacy concerns. The effectiveness of these systems is highly dependent on driver engagement. Systems that generate excessive false alarms or provide unclear feedback are prone to being disregarded by drivers. Finally, the integration of vision-based tools with non-visual monitoring technologies, such as physiological data sensors, offers an opportunity to enhance reliability and address limitations in challenging conditions [27; 28].