### Related Research
These recommendations must be based on each individual's driving behavior, and they are intended to re-educate drivers if they follow incorrect driving patterns (e.g., aggressive driving) like a human instructor would do. To carry out that task, machine learning (ML) techniques, such as fuzzy logic, have been used to give coaching feedback to the driver about his/her performance [34]. Another approach uses artificial neural networks (ANN) to differentiate drivers that are classified among a plethora of driving behaviors, cycles, and scenarios, successfully distinguishing between aggressive and defensive behaviors and urban and highway driving [35]. Several system prototypes for enhancing drivers' braking style employing visual indications have been developed [36]. The authors evaluated the performances of a variety of ML algorithms while using CAN-bus data jointly with non-invasive ECG sensors and smartwatches. Several tests have been performed on these prototypes to validate the improvement of drivers' eco-awareness, and they have yielded promising results. It must be noted that ML-based works on the differences between short-term and long-term DS influence on fuel consumption have been performed with the aim of developing future ADASs, by means of high-quality models that can accurately predict DS-linked consumption [37], or by the identification of the critical maneuvers that cause a rise on the fuel consumption [38]. However, those works neither analyze the driver operations on the car commands (i.e., gas pedal, brake pedal, and gear selector) that cause poor fuel economy nor provide concrete recommendations to improve eco-engaged DS. In light of the foregoing information, several limitations regarding eco-driving assessment systems can be identified. (1) Current in-car systems are intended for generic driving recommendations reporting reduced effectiveness. (2) Most personalized driving assessment systems are based on training sessions and fuel-consumption improvement tracking, normally after the driving sessions, achieving low percentages of fuel economy. (3) ML techniques have been successfully applied in order to classify driving styles into several aggressiveness categories; however, the full potential of these techniques is still unexploited. (4) Most of the existing works in this field do not analyze the handling operations of the driver on the car commands that cause fuel consumption to rise. (5) Providing online DS-based handling recommendations to improve fuel economy is still a mostly unexplored path. Thus, in this work, we present the following contributions to the development of an eco-driving assessment system that is able to provide real-time personalized advice:  
* New applications of unsupervised neural networks to discover particular driving patterns and analyze the effect of driving patterns in fuel-consumption. * A novel approach for the examination of the underlying causes of different types of non-optimal DS from the eco-driving viewpoint and analysis of the fuel-economy-compromising command operations. * Personalization of the provided advices when considering the aforementioned points. Those advices comprehend instructions to improve the use of the gas and brake pedals as well as advice on the shift of the selected gear. * Development of a System-on-Chip with real-time responsiveness. * Improvement in the performance of the already-existing systems, with expected enhancements in both fuel consumption and emissions ranging from the 9.5% to the 31.5%, or even higher for drivers that are strongly engaged with the system.