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Advanced Driver-Assistance Systems (ADASs) are currently gaining particular attention in the automotive field, as enablers for vehicle energy consumption, safety, and comfort enhancement. Compelling evidence is in fact provided by the variety of related studies that are to be found in the literature. Moreover, considering the actual technology readiness, larger opportunities might stem from the combination of ADASs and vehicle connectivity. Nevertheless, the definition of a suitable control system is not often trivial, especially when dealing with multiple-objective problems and dynamics complexity. In this scenario, even though diverse strategies are possible (e.g., Equivalent Consumption Minimization Strategy, Rule-based strategy, etc.), the Model Predictive Control (MPC) turned out to be among the most effective ones in fulfilling the aforementioned tasks. Hence, the proposed study is meant to produce a comprehensive review of MPCs applied to scenarios where ADASs are exploited and aims at providing the guidelines to select the appropriate strategy. More precisely, particular attention is paid to the prediction phase, the objective function formulation and the constraints. Subsequently, the interest is shifted to the combination of ADASs and vehicle connectivity to assess for how such information is handled by the MPC. The main results from the literature are presented and discussed, along with the integration of MPC in the optimal management of higher level connection and automation. Current gaps and challenges are addressed to, so as to possibly provide hints on future developments.

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In the last few years, the automotive industry had to face three main\ud challenges: compliance with more severe pollutant emission limits,\ud better engine performance in terms of torque and drivability and\ud simultaneous demand for a significant reduction in fuel consumption.\ud These conflicting goals have driven the evolution of automotive\ud engines. In particular, the achievement of these mandatory aims,\ud together with the increasingly stringent requirements for carbon\ud dioxide reduction, led to the development of highly complex engine\ud architectures needed to perform advanced operating strategies.\ud Therefore, Variable Valve Actuation (VVA), Exhaust Gas Recirculation\ud (EGR), Gasoline Direct Injection (GDI), turbocharging, powertrain\ud hybridization and other solutions have gradually and widely been\ud introduced into modern internal combustion engines, enhancing the\ud possibilities of achieving the required goals. However, none of the\ud improvements would have been possible without the contextual\ud development of electronics. In fact, that solutions have highly increased\ud the complexity of engine control and management because of the\ud degrees of freedom available for the engine regulation, thus resulting in\ud a long calibration time. In particular, base calibration is the most\ud onerous phase of the engine control, both in terms of experimental and\ud computational effort and costs. This paper addresses some critical\ud issues concerning the calibration of control parameters through the use\ud of a specific Model-Based Computer Aided Calibration algorithm\ud developed by the authors to automate the calibration process and\ud minimize calibration errors. The proposed methodology is also based\ud on the use of neural networks (NN). In particular, starting from a\ud reduced number of experimental data, NN provide a detailed engine\ud data sheets used as input to the actual calibration process itself. The\ud proposed algorithm provides optimal portability and reduced\ud calibration time. The research also highlights how the developed\ud methodology could be useful to identify possible enhancements for\ud specific ECU engine models that can improve the accuracy of the\ud calibration process by using more detailed physically based functions.\ud The results of the proposed research clearly highlight how, in engine\ud control, more accurate physical modeling may lead to promising results\ud and better performance, ultimately enhancing the accuracy, time,\ud experimental effort and cost savings of the calibration process