# Monitoring Technologies for Truck Drivers: A Systematic Review of Safety and Driving Behavior
## 3 Results
### Monitoring Technologies
#### 3.3.2 In-Vehicle Monitoring Systems  
In-vehicle monitoring systems (IVMSs) are recognized as effective tools for improving road safety among professional truck drivers. These systems incorporate technologies such as real-time feedback mechanisms, behavioral analytics, and data monitoring to address unsafe driving behaviors and enhance operational safety. Research has shown that IVMSs, combining in-cab warning lights and supervisory coaching using video footage, lead to measurable reductions in unsafe behaviors. A study involving 315 vehicles reported a significant reduction in risky behaviors when both mechanisms were employed, compared to using warning lights alone. This approach facilitates immediate feedback and supports behavioral adjustments, contributing to improved safety outcomes [19].  
The integration of GPS technology with IVMSs has enabled the monitoring of driver behavior under various operational conditions. A study analyzing data from 4357 trucks identified distinct driving styles--aggressive, normal, and cautious--using clustering methods. The findings revealed that aggressive driving behaviors under heavy-load conditions were associated with elevated risks, highlighting the potential of IVMS to tailor interventions based on driving styles and load conditions [20].  
IVMSs also facilitate the tracking of safety-critical events (SCEs), such as hard braking and near-collisions. A large-scale naturalistic study involving 496 drivers and 13 million miles of driving data applied point process models to analyze these events. The results indicated that SCE frequency increased with driver fatigue and insufficient rest breaks. This suggests that IVMSs can identify periods of heightened risk and support timely interventions to mitigate potential incidents [21].  
Vision-based monitoring systems extend the capabilities of IVMSs by enabling the detection of distractions and other unsafe behaviors. These systems employ algorithms to track hand and head movements, allowing for the identification of manual distractions, such as improper steering wheel grip. Real-time detection of such behaviors enhances the effectiveness of IVMSs in addressing contributing factors to accidents [22].  
Incorporating psychological and physiological metrics into IVMSs further enhances its functionality. Studies have demonstrated that driver anger and fatigue are significant factors influencing driving safety. For instance, a longitudinal study using GPS data found that increased levels of anger were associated with speeding behaviors. Similarly, fatigue-related metrics, such as heart rate variability, were found to correlate with an elevated risk of rear-end collisions. Integrating these metrics into IVMSs allows for tailored recommendations, such as suggesting rest breaks or adjustments to driving behavior [23,24].  
Data logging capabilities within IVMSs enable detailed post-event analyses of driving patterns. Systems that record variables such as speed, acceleration, and lane changes provide insights into recurring risky behaviors. These analyses allow fleet managers to implement targeted training programs and revise safety policies to reduce the likelihood of future incidents [25].  
Evidence highlights the role of IVMSs in reducing unsafe driving behaviors, identifying context-specific risks, and enabling proactive interventions. However, challenges such as high implementation costs, privacy concerns, and resistance from drivers may limit adoption. Addressing these challenges is critical to maximizing the potential of IVMSs for improving road safety among professional truck drivers.