Contents I. Introduction
Drowsiness detection is a very critical domain of research with the consequences concerning driver safety. Fatigue-related accidents are major contributors to road fatalities, with drowsy driving reportedly contributing to several thousand accidents worldwide each year. The risks associated with such incidents can be minimized to a great extent by effective drowsiness detection systems, cautioning drivers much before dangerous levels of fatigue are reached. Traditional systems would depend, in common practice, either on physiological signals, such as eyeblink patterns or behavioral cues like head nodding. However, all these can be invasive or lack efficiency under different environmental conditions; therefore, the interest in developing non-invasive, real-time detecting systems is a main point. [1] Albadawi, et al. It is a very difficult task to detect drowsiness correctly under practical conditions: changes in lighting conditions, different facial features, and operational demands on the fly are rather tough for all systems working on this. Most of them either employ the detecting of blink rates or monitoring eye closure, which all in all is not very reliable, especially in low-light conditions [2] Stancin, et al. Furthermore, drowsiness remains something subjective and varies within human beings; therefore, it is boringly tiresome to design one-size-fits-all solutions. None of the previously discussed techniques have emerged from rule-based systems or simple machine learning models. The thresholds of physiological markers, such as eye-blink rates or head movements, were used by the rule-based system, although this one cannot be easily adapted across different persons and conditions [3]. Siddiqui et al. While the traditional models, such as Support Vector Machines and Decision Trees, are conventionally used for feature-based drowsiness classification, from the conventional models, complicated feature extraction is usually required, and these models are not good at handling large and nonlinear datasets in a usual way [4] Arunasalam, et al. Due to this, their performances are usually far from optimal when applied to a real-time or complicated driving environment. Recent breakthroughs in deep learning-in particular, Convolutional Neural Networks-can help overcome these challenges. These CNNs can inherently originate features from the raw image data, which finally turns them very effective in analyzing facial cues associated with drowsiness. Unlike classical models, these CNNs can adapt to different conditions and subjects, thus offering a scalable solution for real-time drowsiness detection. Several works have been carried out in this respect, where it has been depicted that CNNs can give very good performance in this respect; non-invasive systems have been seen to detect drowsiness from facial data with high accuracy. [5] Hussein, et al. Despite these advantages, some limitations persist. Most CNN-based driver drowsiness detection systems are trained on small, curated datasets, which hurts their generalizability across wide ranges of environmental conditions and diverse populations of drivers kommen Hernandez-Matamoros et al. The computational complexities of running deep learning models in real time raise challenges for implementing such systems in vehicles. Furthermore, overfitting is of concern-if a model learned too well on the training data, this could affect its generalization on new, unseen data. Also, the “black-box” nature of CNN would increase difficulty in explaining the features that this neural network uses for the classification process, and this might raise some transparency-related and trust-related suspicions about the system. The presented paper proposes a CNN-based driver drowsiness detection method, utilizing publicly available real-time facial image datasets. It ranges from developing a non-invasive system to detect drowsiness with high accuracy and evaluating its performance based on metrics of accuracy, precision, and recall to proposing a CNN model that will be compared to traditional machine learning methods to assess the robustness of the proposed approach across different environments and driver profiles. These results shall help explain the potential of CNNs in naturalistic drowsiness detection and give valuable insights into further improvements.
