In using machine learning to characterize driving behavior, there are two main steps.
To characterize driving behavior, a machine learning-based model analyzed data from acceleration (smartphone sensor), OBD-II, and the IMU.
A specific study was then conducted to characterize driving conditions and vehicle usage as a function of vehicle categories, as well as to derive driving cycles specially designed for high- and low-powered cars which have significantly different driving conditions.
Our goal is to describe a method to characterize driving forces both from LULC and dominant LULC changes, recognizing that the presence of certain LULC classes as well as the processes of transition to other uses are both sources of stress with potential effects on the condition of water bodies.
Characterizing driving styles of drivers using in-vehicle sensor data is an interesting research problem and an essential real-world requirement for automotive industries.
X Japan's music developed in the wake of American and British glam and heavy metal music and is characterized by driving power metal compositions with symphonic elements (e.g. "Kurenai", "Silent Jealousy") and emotional ballads (e.g. "Endless Rain", "Forever Love").
To refine the identity of candidate pheromone receptor neurons further, we screened a subset of the Janelia enhancer fragment GAL4 lines (Jenett et al., 2012) that were characterized as driving sparse patterns of expression in anterior sensory neurons (Li et al., 2013).
The proposed method is based on the fusion of visual and sensor features to characterize related driving session and to decide whether the session involves aggressive driving behaviour.
A computational strategy is developed to characterize the driving force for fatigue crack nucleation at subsurface primary inclusions in carburized and shot peened C61® martensitic gear steels.
Considering all features that characterize the driving session, their variation pattern in a certain amount of time is more informative for us rather than the time series signal itself in terms of driver aggressiveness.
