A context aware system for driving style evaluation by an ensemble learning on smartphone sensors data

Rapid Driving Style Recognition in Car-Following Using Machine Learning and Vehicle Trajectory Data

“…A larger size of the network could lead to a long training time [12]. The tree-like structures including decision tree algorithm [13] and Random Forest algorithm [14] are also adopted to detect the driving behaviors according to the extracted features. Some researchers proposed Hidden Markov Model (HMM) to effectively detect dangerous driving behaviors.…”

“…However, risky behavior or accident is hardly observable in daily traffic. Therefore, driver self-reported questionnaire [39] and expert scoring [13] are also adopted to evaluate driving style. However, these two methods rely on subjective judgments of drivers or experts and can be very time-consuming when the number of drivers in the sample is hundreds or even thousands.…”

A Systematic Methodology to Evaluate Prediction Models for Driving Style Classification

“…In addition, in the area of advanced driver-assistance systems (ADAS), researchers are interested in the development of intelligent tools to provide tailored feedback according to the driver's behavior [11].Current studies have reported different methodologies to classify driving styles using in-vehicle data. These methodologies mostly depend on (i) the inputs that can be extracted and derived from the data gathered (e.g., acceleration, deceleration, brake) [12,13]; (ii) the computational models to classify driving styles [14,15]; (iii) driving-styles outputs (e.g., calm, normal, aggressive) [16,17]; and (iv) the performance metrics that evaluate these models [18,19]. Although these studies show a growing interest in classifying driving styles empirically, few studies have systematically evaluated which inputs and models are better predictors of a particular driving style's output.The existing body of research has attempted to evaluate several computational models to identify driving styles using well-established metrics, for example, accuracy, precision, and recall.…”

“…Although these studies show a growing interest in classifying driving styles empirically, few studies have systematically evaluated which inputs and models are better predictors of a particular driving style's output.The existing body of research has attempted to evaluate several computational models to identify driving styles using well-established metrics, for example, accuracy, precision, and recall. These studies have reported that Random Forests (RF), Artificial Neural Networks (ANN), k-Nearest Neighbors (kNN), and Support Vector Machine (SVM) are the best classifiers for estimating driver events [14], driving-style maneuvers [19], and driver's aggressiveness [18]. However, few empirical works have attempted to select the best model across different driving-styles classifiers based on reviewed literature and current research in the field.A review conducted by Meiring and Myburgh [20] suggested that fuzzy logic and SVM can be considered the most promising simple classifiers (c.f.…”

“…The existing body of research has attempted to evaluate several computational models to identify driving styles using well-established metrics, for example, accuracy, precision, and recall. These studies have reported that Random Forests (RF), Artificial Neural Networks (ANN), k-Nearest Neighbors (kNN), and Support Vector Machine (SVM) are the best classifiers for estimating driver events [14], driving-style maneuvers [19], and driver's aggressiveness [18]. However, few empirical works have attempted to select the best model across different driving-styles classifiers based on reviewed literature and current research in the field.…”