## 3 State of the Art  
Automated learning allows for a great amount of information to be processed, classified, and correlated. Autonomous driving systems rely on data from several sensors being delivered at a high rate; this information is filtered, processed, and used by the autonomous driving system for real-time decision making. A high processing capacity onboard vehicles and the volume of data acquired by autonomous driving systems can be used for correlating energy consumption with driving and road data, which is valuable information for improving the energetic efficiency of the vehicle. Vehicle connectivity can greatly expand the impact of vehicles collecting data by allowing them to share this information with other vehicles and central data servers for it to be used for system improvements that can then be downloaded back to all vehicles as system enhancement updates. The use of ML algorithms for processing vehicle data and classifying driving information has been reviewed by [PERSON] et al. [9]. The authors reviewed and grouped 86 works from 46 different journals and conferences according to the ML technics employed in each one (See Figure 1). The review provided an insight in the extended capabilities of ML tools, comparing the results of each work according to metrics such as accuracy, precision, and recall. The review of [PERSON] et al. concluded that the selection of the best ML technic in automotive data classification applications depends on the particularities of the taskto perform, the conditions of the experiments conducted, and the characteristics of the available data. The tendency of cars innovations aims toward the realization of the holistic view of its role in the modern society, where the vehicle is not only an element to move from one point to another, but it is fully integrated in human daily experiences and its environment [10]. MPC is a powerful method for vehicle control, but its computing performance can be improved by integrating machine learning technics into its functioning [11]. ACC is a useful functionality that allows for increased comfort and as an additional safety measure during driving. As ACC corresponds to level one autonomous driving, its functionality is closely affected by human interaction in the context of the vehicle itself, beside the interactions with exterior agents such as pedestrians and drivers of other vehicles. So, the driver is a key factor that weighs into the ACC performance. Quantifying the effect of the driver is of utter importance as stated in other works regarding the research of ACC technology [12]. Driver behavior can account for up to 30% more energy consumed when comparing aggressive and moderate driving styles [13]. Additionally, the operating characteristics of the individual drivers require an ACC that best adapts to them, for a safer, more comfortable, and more efficient driving experience [12]. Eco-driving involves applying a set of strategies to reduce the vehicles total energy consumption for a given displacement. As seen in the previous section, there are many approaches for improving the energy consumption of a vehicle. [PERSON] et al. [14] groups the driving behavior-related approaches for improving energy efficiency into: Eco-routing, using road slope information, traffic light assistance, platooning, and overtaking. Real-time awareness of the driving environment is a valuable input for an online energy management optimization system in a vehicle. Planning ahead for a specific route could ideally allow a minimized energy consumption driving strategy to be prepared, but there are always changing factors along the route, such as traffic and red lights at intersections that need to be accounted for by means of probabilistic calculations or real-time adaptation [15]. Artificial Intelligence can be a suitable tool for improving the energy efficiency of vehicles in complex scenarios and ever-changing roadway and traffic conditions [16].