## 2 Literature Review  
The effective integration of Advanced Driver Assistance Systems (ADASs) into modern vehicles presents a dual challenge: developing robust technology and ensuring that drivers can use it effectively. The literature reveals significant advancements in ADAS technology itself, yet also highlights persistent issues in driver training and performance assessment. To build a case for our study, this review will first examine recent technological solutions for vehicle and driver monitoring, then explore studies on evaluating driver behavior and training, which will ultimately lead to identifying a critical gap in objective assessment methods. The article [7] presents a state-of-the-art pedestrian detection system at night for autonomous vehicles and driver assistance systems (ADASs). It uses a fusion of RGB and thermal imaging camera data to effectively detect pedestrians in difficult lighting conditions. Images from both cameras are automatically matched using an algorithm that compensates for differences in resolution and field of view, eliminating the parallax effect. The data is combined using a late-fusion method, which provides the best compromise between accuracy and speed. Using the YOLOv5 algorithm and transfer learning, the system generates accurate pedestrian detection frames. Optimization using TensorRT has increased performance to 33 frames per second on an NVIDIA Jetson Orin device, enabling real-time processing. The article [8] presents a system that enables automatic detection and classification of critical traffic situations on highways, which supports the safety assessment of driver assistance systems (ADASs) at level 3 autonomy according to SAE classification. At this level, the system can take full control of the vehicle under certain conditions, such as when driving on a highway, without the need for constant driver supervision. The system relies on data from radar, lidar, and cameras, recording longitudinal and transverse distances at 50 Hz. It compares two approaches to scenario classification: a traditional rule-based model (decision tree) and a modern approach using machine learning--Time Series Forest (TSF). The TSF model was trained on so-called idealized vehicle maneuvers, generated using Hermite-spline regression, which reflect typical traffic scenarios. The TSF-based solution showed higher precision and sensitivity in identifying critical events compared to the rule-based model, also performing better in the presence of data noise. The use of such methods enables more effective evaluation of ADAS and supports the development of safer autonomous driving technologies. These studies [7,8] exemplify the technological sophistication of modern ADAS, but their real-world effectiveness hinges on the driver's interaction with these systems, particularly under challenging conditions. To that end, other research focuses on monitoring the driver directly. The article [9,10] describes the on-board unit (OBU) system integrated with the vehicle, supporting the driver through warnings about speeding and dangerous curves, as part of Vehicle-to-Infrastructure (V2I) communication. The system was tested in the SimEASY driving simulator on a virtual version of the winding country road SS113 (Messina, Italy) with a width of 7 m. The described system is aimed at improving safety in dangerous road conditions, such as passing groups of cyclists. The experiment involved 14 drivers, analyzing their visual behavior (using the Tobii Glass eye tracker) and workload (NASA TLX) in four scenarios. NASA TLX is a method for assessing the subjective workload of a user during the performance of an assigned task, developed by NASA scientists. The system displays visual warnings on a screen divided into four zones, signaling inappropriate behavior or key elements of the route, and remains inactive when the driver behaves correctly.