A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems

Alessia Musa, Michele Pipicelli, Matteo Spano, Francesco Tufano, Francesco De Nola, Gabriele Di Blasio, Alfredo Gimelli, Daniela Anna Misul and Gianluca Toscano
Additional contact information
Alessia Musa: Department of Mechanical and Aerospace Engineering (DIMEAS), Politecnico di Torino, 10125 Torino, Italy
Michele Pipicelli: Department of Industrial Engineering, University of Naples Federico II, 80126 Napoli, Italy
Matteo Spano: Department of Mechanical and Aerospace Engineering (DIMEAS), Politecnico di Torino, 10125 Torino, Italy
Francesco Tufano: Department of Industrial Engineering, University of Naples Federico II, 80126 Napoli, Italy
Francesco De Nola: Teoresi S.P.A., 10152 Torino, Italy
Gabriele Di Blasio: Istituto di Scienze e Tecnologie per l’Energia e la Mobilità Sostenibili (STEMS), 80125 Napoli, Italy
Alfredo Gimelli: Department of Industrial Engineering, University of Naples Federico II, 80126 Napoli, Italy
Daniela Anna Misul: Department of Mechanical and Aerospace Engineering (DIMEAS), Politecnico di Torino, 10125 Torino, Italy
Gianluca Toscano: Teoresi S.P.A., 10152 Torino, Italy

Energies, 2021, vol. 14, issue 23, 1-24

Abstract: 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.

Keywords: optimal control; model predictive control; Advanced Driver-Assistance Systems; connected vehicle; cruise control; lane keeping; path following (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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