Elevating Driver Behavior Understanding With RKnD: A Novel Probabilistic Feature Engineering Approach

Early detection of driver behavior is a pivotal aspect in enhancing road safety, focusing on identifying and mitigating risky driving patterns before they lead to accidents. The use of smartphone sensors for data acquisition marks a significant advancement in this field. It allows for continuous, real-time monitoring of driving patterns without the need for specialized equipment. In this study, we leverage a publicly available smartphone motion sensor dataset, utilizing accelerometer and gyroscope data from a Samsung Galaxy S21 to analyze driving behaviors classified as slow, normal, and aggressive. This research introduces a novel feature engineering technique named the RKnD (Random forest, K-nearest classifier, Decision tree) probabilistic feature engineering technique, which integrates three prominent machine learning (ML) models. This blend offers a robust analysis of driver behavior, leveraging the strengths of each algorithm. This paper emphasizes the importance of data balancing in machine learning, employing the Synthetic Minority Oversampling Technique (SMOTE) to enhance the reliability of the predictions. Furthermore, k-fold cross-validation is used to ensure the model’s consistency and accuracy across original features and the proposed RKnD probabilistic features of the data sets. By achieving such high accuracy, the study demonstrates the potential of smartphone-based systems to significantly improve road safety. This paper introduces a novel approach utilizing smartphone motion sensor data to detect driver behaviors with a remarkable accuracy rate of 99.63%. This research stands out for its application of machine learning techniques in a practical, accessible manner. This pioneering approach named RKnD feature engineering sets a new standard in the realm of smart transportation systems, opening avenues for further innovations in the field, and filling a gap in road safety analysis to avoid road accidents. Future research on RKnD should streamline its algorithm for real-time use, diversify datasets, integrate advanced Deep Learning for complex pattern detection, and undertake real-world testing to validate practicality and uncover challenges.