Abstract-The DrivingStyles architecture integrates both data mining techniques and neural networks to generate a classifica tion of driving styles by analyzing the driver behavior along each route. In particular, based on parameters such as speed, acceleration, and revolutions per minute of the engine (rpm), we have implemented a neural network based algorithm that is able to characterize the type of road on which the vehicle is moving, as well as the degree of aggressiveness of each driver. The final goal is to assist drivers at correcting the bad habits in their driving behavior, while offering helpful tips to improve fuel economy. In this work we take advantage of two key-points: the evolution of mobile terminals and the availability of a standard interface to access car data. Our DrivingStyles platform to achieve a symbiosis between smartphones and vehicles able to make the former operate as an onboard unit. Results show that neural networks were able to achieve a high degree of exactitude at classifying both road and driver types based on user traces. DrivingStyles is currently available on the Google Play Store platform for free download, and has achieved more than 1550 downloads from different countries in just a few months.
Meseguer et al. proposed DrivingStyles REF , a solution combining an Androidbased application and a web platform that is able to determine the type of road where the driver is circulating, as well as his driving habits.
DrivingStyles: A smartphone application to assess driver behavior
2013 IEEE Symposium on Computers and Communications (ISCC)
2013 IEEE Symposium on Computers and Communications (ISCC)
Computer Science