… to quantify instantaneous driving behavior and classify driving styles in different spatial
contexts using unsupervised machine learning methods. To this end, a subset of the Safety Pilot …

… initially be classified as supervised, semi-supervised, unsupervised, or reinforcement learning…
With the major influx of attention devoted to the ML-based analysis of driving behavior and …

… The main purpose of this work is to apply machine learning techniques to identify unsafe
driving behaviors by taking … On the other hand, unsupervised models hardly manage to produce …

… applying unsupervised machine learning techniques, we can understand a driver’s response
within each driving behavior… driving behavior depicts that most of the drivers of our study …

… for this specific application of driver behaviour classification. It … clustering is an unsupervised
machine learning algorithm, by … efforts on driver behaviour analysis using machine learning …

… This paper presents a novel yet efficient deep learning method for analyzing the driver behavior.
… They use unsupervised algorithm (ie., k-mean) and supervised algorithm (ie., SVM) for …

… [17] proposed to use an unsupervised anomaly detection method, namely Support Vector …
neural networks (DNN) are a powerful subclass of machine learning models that use several …

… ABSTRACT In this study, an improved deep learning model is … the road environment and
driver’s behaviour throughout the … several applications including tasks based on unsupervised …

… and machine learning models are then used to classify a driver’s … The unsupervised learning
framework presented in this … an unsupervised decision support system that uses extracted …

… diverse applications. The available techniques have been previously classified depending on
labeling or not (unsupervised … The algorithms previously used in driving applications either …