## 2 Literature Review
This ADAS (OBU) system helps drivers in moderate driving situations, such as when driving on a winding road with cyclists, by reducing speeding, but loses effectiveness in simple or extremely difficult conditions when drivers bypass the device to concentrate on the road. The article [11] presents a deep learning-based driver drowsiness detection technique (DLID3-ADAS) aimed at increasing road safety through integration with Advanced Driver Assistance Systems (ADASs). The presented DLID3-ADAS technique is based on a multi-stage process of detecting driver drowsiness, using deep machine learning along with hyperparameter optimization. Initially, images from the vehicle's on-board cameras are pre-processed using a filter that reduces noise and artifacts by replacing the pixel intensity values with the median values in their local vicinity, improving the quality of input data for further analysis. For optimal selection of ShuffleNet hyperparameters, such as learning rate and lot size, the Northern Goshawk Optimization (NGO) algorithm is used, inspired by the hunting behavior of the Northern Hawk. The NGO algorithm systematically searches the parameter space, improving the accuracy of the model, reducing overfitting, and increasing its ability to generalize. In the final stage, the Extreme Learning Machine (ELM) model, based on single-layer feedforward networks, classifies the driver's state as \"sleepy\" or \"alert\", offering fast and precise predictions thanks to simplicity and low computational requirements. Feedforward networks are networks in which information flows in one direction from the input layer, through a single hidden layer, to the output layer, without feedback loops or cycles. As a result, the system generates alerts, such as audible or visual signals, when drowsiness is detected, allowing the driver to take action to prevent accidents. The entire process is optimized for real-time operation, making it suitable for Advanced Driver Assistance Systems (ADASs). The article [12] presents a methodology for evaluating the effectiveness of an onboard unit (OBU) in improving road safety by analyzing its effect on driver behavior under simulated conditions. A study of 14 young drivers showed that OBU does not increase psychophysical load, as confirmed by telemetry data and NASA TLX survey results. The presence of cyclists does affect driving parameters, but OBU does not worsen these responses, and in some cases, promotes more stable driving. The authors emphasize the need for different measures of load and dynamic adjustment of support systems to road conditions and driver characteristics. A limitation of the study is the small sample size and specific route. The results can support road managers in identifying dangerous sections and planning measures to improve safety. While these technological systems provide crucial support, the literature underscores a fundamental gap between system availability and driver proficiency. Despite the widespread use of driver assistance systems, potential users have very little knowledge about them. About 180 people from the group of drivers and fleet managers took part in the survey conducted by the Motor Transport Institute. As many as 52.3% of participants asked about the source of their knowledge about ADAS admitted that they learned by trial and error. At the same time, participants admitted that they did not take part in the training, but would gladly do so if it were available [13; 14; 15]. Currently, there is a lack of reliable materials from which drivers could draw their knowledge. Instructions, although often containing errors, are still the main source of information. Drivers and dealers are not aware of the benefits and risks of using ADAS. So far, no requirements have been introduced as to drivers' skills in the field of their proper use. There is also a lack of a clear approach to assessing the quality and effectiveness of training [16]. Training drivers in the proper use of ADAS seems to be crucial for road safety [15; 16; 17].