Matute-Peaspan, Jose A.Marcano, MauricioDiaz, SergioZubizarreta, AsierPerez, Joshue2020-10-13Matute-Peaspan , J A , Marcano , M , Diaz , S , Zubizarreta , A & Perez , J 2020 , ' Lateral-acceleration-based vehicle-models-blending for automated driving controllers ' , Electronics (Switzerland) , vol. 9 , no. 10 , 1674 , pp. 1-17 . https://doi.org/10.3390/electronics9101674 , https://doi.org/10.3390/electronics91016742079-9292researchoutputwizard: 11556/1005Publisher Copyright: © 2020 by the authors. Licensee MDPI, Basel, Switzerland.Model-based trajectory tracking has become a widely used technique for automated driving system applications. A critical design decision is the proper selection of a vehicle model that achieves the best trade-off between real-time capability and robustness. Blending different types of vehicle models is a recent practice to increase the operating range of model-based trajectory tracking control applications. However, current approaches focus on the use of longitudinal speed as the blending parameter, with a formal procedure to tune and select its parameters still lacking. This work presents a novel approach based on lateral accelerations, along with a formal procedure and criteria to tune and select blending parameters, for its use on model-based predictive controllers for autonomous driving. An electric passenger bus traveling at different speeds over urban routes is proposed as a case study. Results demonstrate that the lateral acceleration, which is proportional to the lateral forces that differentiate kinematic and dynamic models, is a more appropriate model-switching enabler than the currently used longitudinal velocity. Moreover, the advanced procedure to define blending parameters is shown to be effective. Finally, a smooth blending method offers better tracking results versus sudden model switching ones and non-blending techniques.171184008enginfo:eu-repo/semantics/openAccessjournal article10.3390/electronics9101674Automated drivingModel predictive controlTrajectory trackingVehicle controlVehicle-model blendingControl and Systems EngineeringSignal ProcessingHardware and ArchitectureComputer Networks and CommunicationsElectrical and Electronic EngineeringSDG 11 - Sustainable Cities and CommunitiesProject IDinfo:eu-repo/grantAgreement/EC/H2020/737469/EU/Advancing fail-aware, fail-safe, and fail-operational electronic components, systems, and architectures for fully automated driving to make future mobility safer, affordable, and end-user acceptable/AUTODRIVEinfo:eu-repo/grantAgreement/EC/H2020/737469/EU/Advancing fail-aware, fail-safe, and fail-operational electronic components, systems, and architectures for fully automated driving to make future mobility safer, affordable, and end-user acceptable/AUTODRIVEFunding InfoThis research was funded by AUTODRIVE within the Electronic Components and Systems for European Leadership Joint Undertaking (ECSEL JU) in collaboration with the European Union’s H2020 Framework Program (H2020/2014-2020) and National Authorities, under Grant No. 737469This research was funded by AUTODRIVE within the Electronic Components and Systems for European Leadership Joint Undertaking (ECSEL JU) in collaboration with the European Union’s H2020 Framework Program (H2020/2014-2020) and National Authorities, under Grant No. 737469http://www.scopus.com/inward/record.url?scp=85092496615&partnerID=8YFLogxK