# Monitoring Technologies for Truck Drivers: A Systematic Review of Safety and Driving Behavior
## 3 Results
### Monitoring Technologies
#### 3.3.4 Advanced Driver Assistance Systems  
Advanced driver assistance systems (ADASs) play a pivotal role in enhancing the safety of commercial truck drivers by integrating technologies that assist in real-time decision-making and mitigate potential risks. Studies demonstrate that these systems, which include features like FCWs, lane departure warning (LDW), headway monitoring and warning (HMWs), and speed limit indicators (SLIs), significantly influence driver behavior and safety outcomes.  
The FCW system, as analyzed by [PERSON] and [PERSON] [32], optimizes truck safety by issuing alerts based on driver response behavior. Their study categorized driver responses into three clusters: Response Before Warning, Response After Warning, and No Response. By tailoring warning distances to these clusters using machine learning techniques, the optimized FCW system demonstrated a 97.92% accuracy and reduced false alarm rates to 1.73%, improving overall safety outcomes by up to 5%.  
In a naturalistic driving study by [PERSON] et al. [33], ADASs, particularly FCW and LDW, were evaluated for their impact on real-world truck driver behavior. The study found that the activation of these systems led to a reduction in warnings issued for speeding and lane departures, correlating with improved safety behaviors. For instance, LDWs effectively decreased lane departure warnings by 28%, while HMWs reduced insufficient headway warnings by 45%.  
The haptic-based lane-keeping assistance (LKA), evaluated by [PERSON] et al. [34], further highlights the efficacy of continuous and bandwidth-based steering aids. The study found that continuous haptic assistance provided superior lane-keeping performance compared to bandwidth assistance, particularly under conditions of driver distraction. This underscores the importance of designing systems that adapt to varying levels of driver attention and workload.  
[PERSON] et al. [35] explored how ADASs function under supervisory monitoring in a military setting, specifically with drivers diagnosed with attention deficit hyperactivity disorder (ADHD). The results revealed that drivers with ADHD exhibited significantly higher rates of safety events despite the implementation of ADASs, highlighting the necessity for tailored interventions and system adjustments for specific driver populations.  
Similarly, [PERSON] and [PERSON] [36] examined the distraction and workload implications of connected vehicle (CV) warnings in ADASs. They noted that while spot weather warnings were well-received, work zone warnings caused prolonged off-road glances, potentially compromising safety. These findings emphasize the need for refined human-machine interface (HMI) designs to reduce distraction while maintaining effectiveness.  
[PERSON] et al. [37] addressed the predictive capabilities of ADASs using machine learning models to forecast safety-critical events (SCEs) up to 30 min in advance. By leveraging extensive kinematic data, the study achieved a predictive accuracy of 76%, offering a proactive approach to mitigate risks associated with unsafe driving behaviors.  
ADAS's capacity to address interactions with vulnerable road users was demonstrated by [PERSON] and [PERSON] [38]. Their test-track experiment analyzed truck drivers' kinematic and visual behavior when encountering cyclists and pedestrians. The findings inform the design of ADASs for better detecting and responding to such scenarios, thereby reducing collision risks.  
Collectively, these studies underscore the transformative potential of ADASs in reducing risks and enhancing safety for truck drivers. However, challenges remain, including addressing system limitations such as false alarms, driver over-reliance, and workload imbalances. Continued advancements in ADAS design, coupled with tailored training and deployment strategies, will be crucial in maximizing their effectiveness in diverse operational contexts.