## 1 Introduction  
As transport technology evolves, new opportunities arise. Formerly, the efficiency of energy usage in street vehicles was close to being maxed out up to a point limited by the physical principles of the internal combustion engine. With the renewed interest in electric vehicles (EVs) and the mass production of them, a vast new field for improvement is to be worked on by research engineers, physicists, and designers of many areas, ranging from batteries technology, charging stations, power train mechanics, and of course vehicle energy management. This era in vehicle technology has seen the advent of two major paradigm changes, the electrification of street vehicles and the implementation of higher levels of autonomous driving, thanks to the increased availability of high computational power on board in the vehicles and cloud computing connectivity.  
There is interest in increasing vehicle energy efficiency for environmental, technical, and economic reasons. Environmentally, all energy taken from the power grid contains a portion that comes from fossil fuels with the consequential undesired contamination. These proportions in the energy origin varies greatly from one country to another. The technical and economic reasons come together as car manufacturers want to improve the mileage per unit of energy of their vehicles through technological development and to offer a better option to customers. Besides, there are also governmental regulation policies in many countries aimed to directly stimulate the development of these technologies and reduce emissions.  
The development of autonomous, intelligent, and connected vehicles and its integration with eco-driving technics have the potential to further increase the energy efficiency. The integration of all the aforementioned methods comprises a synergy of great interest for studying, evaluating, and implementing, taking energy usage optimization to new high possibilities.  
This document explains the procedure and method employed to formulate, develop, and test an ML Classification Algorithm for an effective real-time driving environment classification. The data was collected from three hundred tests consisting of driving in an electric vehicle in a 1.5 km route inside our university campus while recording energy and vehicle dynamics data using specialized instrumentation (Dewesoft's SIRIUS Data acquisition System). The tests included eight different drivers and three different \"driving styles\". The collected data was then analyzed and processed to obtain composite variables that provided more information to the algorithm. Several ML technics were tested on the pre-processed dataset and evaluated based on training time, prediction time, and classification scores. The main contributions of this work include the proposal and assessment of a methodology, the identification of key features for driving scenario classification tasks, and the evaluation of results for determining if such a system can be useful for intelligent driving systems.  
This paper is organized as follows: Section 2 describes the previous works, and the state of the art is presented in Section 3. Section 4 presents the methodology employed in this work and details of the experimental data acquisition including the EV used for the tests, the route, and the sensors. Section 5 presents the features used and the data pre-processing. Section 6 describes the data analysis and the initial classification with Machine learning algorithms. Section 7 presents further classification tasks while also discussing its results. Finally, the work's conclusions are drawn in Section 8 and future work is proposed in the last section.