A machine learning framework for real-time turn detection for enhancing driving safety using smartphone sensors

Abstract Reckless driving behaviors, including abrupt braking, sudden acceleration, and sharp vehicular turns, significantly elevate the risk of road traffic incidents and fatalities. To address this, the present study introduces a machine learning-based framework for real-time detection, classification, and categorization of turn maneuvers using orientation data acquired from commodity smartphone sensors. The proposed system offers a low-cost, infrastructure-independent solution for analyzing driver behavior in urban mobility environments. Empirical data were collected from two riders traversing a predefined route within the urban landscape of Durgapur, India. The methodology focuses on the x -axis orientation sensor, which effectively captures lateral motion during vehicular turns. After preprocessing and feature engineering, a logistic regression model was trained to detect turning events, achieving a classification accuracy of 91%. For directional turn classification (left/right), a decision tree classifier was employed, yielding an accuracy of 75%. Furthermore, Dynamic Time Warping (DTW) was utilized to compute similarity metrics between observed and reference turn signatures, facilitating post-classification categorization into quality-of-turn tiers ranging from Very Good Ride to Very Bad Ride . The experimental outcomes validate the efficacy of integrating smartphone-based sensing with lightweight machine learning models for granular driver behavior assessment. The proposed system demonstrates potential for deployment in intelligent transportation systems (ITS) and road safety analytics, thereby contributing to proactive risk mitigation and behavioral intervention strategies.