This study proposes a transparent, data-driven framework for behavior recognition based exclusively on IMU measurements, hypothesizing that vehicular jerk-based features can help in differentiating driving behavior. Unlike studies relying on direct jerk values, our approach derives novel findings from jerk-based features. For rolling windows of 300 samples, a comprehensive set of statistical and dynamic descriptors is extracted, including amplitude, variance, standard deviation, coefficient of variation, standard error, skewness, and kurtosis, as well as jerk-based features such as jerk_std, jerk_variance, jerk_amplitude, and jerk_spikes. Statistical analysis is used to identify features with strong discriminative power. Effect sizes, measured by Cohen’s d, quantify the difference between normal and aggressive driving styles. The selected features are used to compute the Driving Score (DS) and provide a driver’s profile. Experimental results reveal a correlation between lower DS scores (< 50) and windows characterized by high jerk variability, large amplitude fluctuations, and frequent spikes. Conversely, higher DS scores (>70) indicate smooth and stable motion patterns. The robustness of the proposed framework is evaluated using several machine learning classifiers as baselines, with the most important jerk-based features as inputs. For the aggressive driver class, the DBS model reports a Recall of 0.952 and an F1 of 0.925. For the normal driver class, the DBS model reports a Recall of 0.839 and an F1 of 0.879. The model has a total accuracy of 0.907. Also, Logistic Regression and ensemble models like XGB and RF perform well. The proposed framework offers an explainable, computationally efficient alternative to conventional machine-learning classifiers for identifying aggressive drivers.

This study proposes a transparent, data-driven framework for behavior recognition based exclusively on IMU measurements, hypothesizing that vehicular jerk-based features can help in differentiating driving behavior. Unlike studies relying on direct jerk values, our approach derives novel findings from jerk-based features. For rolling windows of 300 samples, a comprehensive set of statistical and dynamic descriptors is extracted, including amplitude, variance, standard deviation, coefficient of variation, standard error, skewness, and kurtosis, as well as jerk-based features such as jerk_std, jerk_variance, jerk_amplitude, and jerk_spikes. Statistical analysis is used to identify features with strong discriminative power. The selected features are used to compute the Driving Score (DS) and, along with the Kernel Density Estimation (KDE) and associated statistics, provide a driver’s profile. Low DS values are consistently associated with increased jerk variability, whereas high DS values correspond to smoother and more controlled motion profiles. The robustness of the proposed framework is evaluated using several machine learning classifiers as baselines, with the jerk-based features as inputs. For the aggressive driver class, the Driving Behavior Score (DBS) model reports a Recall of 0.952 and an F1 of 0.925. For the normal driver class, the DBS model reports a Recall of 0.839 and an F1 of 0.879. The model has a total accuracy of 0.907. Also, Logistic Regression and ensemble models like Extreme Gradient Boosting (XGB) and Random Forest (RF) perform well. The proposed framework offers an explainable, computationally efficient alternative to conventional machine-learning classifiers for identifying aggressive drivers. It relies on lightweight statistical computations being suitable for real-time implementation.