Abstract:
The safety of drivers is a critical issue in modern transportation, as road users are seriously
endangered by inattentive and sleepy drivers. The goal of this project is to create a sophisticated
driver safety system that can identify and lessen these risks. With the use of real-time image
processing and machine learning algorithms, the system seeks to precisely detect indicators of
fatigue and keep an eye on driver behavior. It gives a proactive way to improve road safety by
utilizing support vector machines and facial feature analysis.
The methodology used for this project entails gathering and preprocessing pertinent data, then
creating and honing machine learning models. The models are trained using supervised learning
paradigms, which allow them to identify patterns suggestive of sleepiness and unpredictable
behavior. Techniques for real-time image extraction and classification are integrated to give
drivers immediate feedback, enabling prompt actions to avoid collisions.
Promising initial results from the prototype implementation are shown, and quantitative metrics
show how effective the suggested system is. Evaluation metrics like area under the receiver
operating characteristic curve (AUC-ROC) and confusion matrices are used to evaluate the
performance of the models in classification tasks. These initial results indicate that there is a lot
of promise for improving road safety and reducing collisions with the Driver Safety System.
