## 3 Related Works  
There are increasing studies in the development of intelligent monitoring systems, such as the fatigue and alertness monitoring system. In [35], a specialized system was devised to monitor the physiological status of drivers, employing computer vision and neural networks. The developed system continuously checks the driver's activities through cabin-installed cameras and promptly issuing warning signals and alarms upon detecting any abnormal driving behaviors or actions. The cameras specifically monitor the driver's head and eye pose, as well as eye rotation. The acquired information and signals undergo analysis via an echo state network (ESN). This neural network runs on an NVIDIA Jetson Nano. While the system attains an accuracy of 98%, perfection remains elusive. The perpetual challenge confronting computer vision-based systems lies in the dynamic nature of visual conditions. In [36], a code was devised to assess both the condition of the road surface and the prevailing weather conditions, like dry, wet, and snowy. This system relies on the vehicle's camera system, operating with a custom-trained neural network (VGG16). Recent research has also focused on studying road surface markings integral to traffic signalization. In their work, [PERSON] et al. [37] proposed a two-stage model employing YOLO-v2 [22] to address the challenge posed by partially distorted and worn road markings. In the initial stage, the YOLO-v2 CNN was tasked with detecting the primary road markings, and in the second stage, a lightweight, and transformation-invariant classification network, RM-Net, which can handle distortions and surface wear, was utilized to identify roadmarkings. The authors have developed an annotated road marking detection dataset consisting of approximately 12,000 high-resolution images categorized into 13 classes, captured under diverse weather conditions during both day and night. The achieved result of the model is notably commendable with an 86.5% mAP on the dataset, thereby surpassing the performance of other existing frameworks. [PERSON] et al. [38] developed an application dedicated to the recognition of pedestrians and vehicles. This system operates in conjunction with an NVIDIA Jetson Nano edge device. Their study incorporates five distinct pre-trained models, namely PedNet, MultiPed, SSD-MobileNet v1 and v2, and SSD-Inception v2. The detection of traffic signalization, encompassing vital road features like traffic signs, traffic lights, and road surface markings, within the vicinity of self-driving cars stands as a fundamental prerequisite for the reliable operation of autonomous driving systems. This crucial feature ensures compliance with traffic regulations and elevates both vehicle and occupant safety. Consequently, an array of recent studies has focused on this research domain, aiming to further improve the robustness and reliability of traffic signal detection algorithms and systems. [PERSON] and [PERSON] [39] proposed a two-stage approach for traffic sign detection. In the initial stage, detection and classification are limited to the circular or triangular shapes of the signs, achieved through HO (Histogram of Oriented Gradients) [40] features and support vector machines (SVM). Subsequently, in the second stage, a CNN is employed to classify these detected shapes into their respective subclasses. Finally, the approach is evaluated on the GTSDB (German Traffic Sign Detection Benchmark) [41], showing improved results. In their system, [PERSON] et al. [42] employed an SSD [21] detector and adapted the Inception-v3 CNN as the base network, instead of the originally enlisted VGG CNN, to enhance both speed and accuracy. The study introduced modifications to the prior box generation, allowing smaller strides in the latter network layers, thereby improving the detection of smaller objects. Non-maximum suppression (NMS) was also incorporated to avoid multiple detections of a single object.