Abstract: This paper presents a comprehensive approach to predicting aggressive driving behaviors using a deep neural network (DNN) model. The model is
trained on a diverse dataset containing both behavioral and environmental metrics, including vehicle specifications, road conditions, weather parameters, and
driver interaction patterns. Key features, including vehicle speed, length, road
lane, weather conditions, and preceding vehicle dynamics, provide a solid foundation for modeling driving behavior. Using carefully preprocessed data, the
model predicts the driving style for each individual trip, classifying it into categories such as cautious, potentially aggressive, and aggressive. The core of the
study lies in the implementation and application of a deep learning model, specifically a multilayer perceptron (MLP), which is designed to capture complex,
non-linear relationships between input variables and driving styles. The architecture incorporates several hidden layers, with dropout and batch normalization
techniques to enhance generalization and prevent overfitting. Additionally, hyperparameter tuning was integrated to further optimize the model's performance.
The model's effectiveness is thoroughly evaluated using a range of performance
metrics, achieving a promising accuracy rate of 87%. The proposed method holds
significant potential for dynamically constructing driver profiles based on realtime driving behavior information, with applications in autonomous driving systems, intelligent vehicle safety technologies, and personalized insurance models,
offering a powerful tool for proactive risk assessment and driver monitoring.
