Eco-driving has acquired great importance in recent years because it is a way to reduce energy consumption that can be applied to any type of vehicle. However, for these rules to be applied requires a process of continuous learning and motivation. For this reason many eco-driving assistants have emerged. This paper presents Green Driving, a driver safety app for Android that detects inattentive driving behaviors and gives corresponding feedback to drivers, scoring their driving and alerting them in case their behaviors are unsafe.  It’s about changing a person’s driving behavior by providing some kind of advice to the driver.  I have worked on an algorithm that is meant to reduce the fuel consumption of users. The algorithm is deployed in an android application. This application “Green Driving” is aimed at users with cars. It is basically like an assistant, suggesting the user when he should make the right gear changes, when to increase/decrease speed and avoids hard braking and rapid acceleration and etc. It is in order to drive economically, ecologic and in turn save money and safety. This is a smart way of letting a user drive economically and ecologic since almost everyone has an Android smartphone now.

Recent advances in technology are changing the way how everyday activities are performed. Technologies in the traffic domain provide diverse instruments of gathering and analysing data for more fuel-efficient, safe, and convenient travelling for both drivers and passengers. In this article, we propose a reference architecture for a context-aware driving assistant system. Moreover, we exemplify this architecture with a real prototype of a driving assistance system called Driving coach. This prototype collects, fuses and analyses diverse information, like digital map, weather, traffic situation, as well as vehicle information to provide drivers in-depth information regarding their previous trip along with personalised hints to improve their fuel-efficient driving in the future. The Driving coach system monitors its own performance, as well as driver feedback to correct itself to serve the driver more appropriately.