## 3 State of the Art
The \"road slope\" energy improving strategies normally involves knowing the road topography of the route beforehand, and performing optimization using this information, together with vehicle dynamics models [17]. [PERSON] et al. also state in their work that the optimized speed trajectories are rarely used directly to provide a reference value for low  
Figure 1: Distribution of ML technique used in vehicle data classification (Data by [PERSON] et al. [9]). level controllers such as cruise control, nonetheless, that is the path in which automotive technology is advancing. The energy consumption of an electric vehicle can be predicted with a precise model of the powertrain that includes all sub-components [18], but an individual model must be developed for each car variation. Some works on the field of energy efficiency create macro models of the whole transportation sector using artificial neural networks for long-term prediction of energy demand [19; 20]. The computation of EVs energy consumption in real-time and short-range predictions have been also studied by other authors while other works study energy performance by considering particular segments of a predefined route or focus on the effect of single variables such as the vehicle weight [21; 22]. Many shortcomings of traditional systems can be overcome by implementing data approaches such as deep learning methods, taking advantage of their ability to find correlations between the given inputs automatically (unsupervised learning) and share this acquired knowledge with other systems [23]. Deep learning algorithms are especially useful for image recognition and classification [24]. The implementation of deep learning in autonomous vehicle applications generally uses deep-neural-network-based controllers, together with perception modules. Sensors provide the input information to the controller algorithm and it outputs the actuation for driving the vehicle safely under predefined rules [5; 25]. Other uses of deep learning for vehicle applications includes the prediction of specific driver speed profiles [26], the prediction of optimal speed profiles for a specific sector on a route, and the prediction-classification of road, traffic, and driving environments with the objective of improving the vehicle energy efficiency [27]. A summary of the reviewed state-of-the-art and the comparison of characteristics with this work is presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Article** & **Authors** & **Functions** & **Model** & **Methods** & **Evaluation** \\ \hline Increasing the Fuel & & & Vehicle Dynamics & Dynamic programming & SIMULATION using models to compare results of different control strategies \\ Economy of Connected & & & & Model, Energy & Dynamic programming & SIMULATION using models to compare results of different control strategies \\ and Autonomous & & & & Model, Energy & Dynamic programming & \\ and Autonomous & & & Model, Energy & Dynamic programming & \\ and Autonomous & & & Model, Energy & Dynamic programming & \\ and Autonomous & & & &  
The works reviewed in the State-of-the-Art section cover a wide range of applications of ML in automotive technology, mainly in the creation of models to predict and act upon vehicle states or, in other cases, to classify the driver according to pre-defined categories of driving styles. These works approach the environment assessment as a computer vision problem or as a historic modeling of roads based on pre-registered information. The real-time assessment and classification into categories are the key points for the new approach proposed in this document. This work proposes a procedure for classifying driving scenarios based on vehicle dynamics and energy sensors data, describing the suitable pre-processing for the raw data and the recommended input variables that should be included to get the maximum amount of information from the data, while minimizing the processing load and processing time in the classification system.