Lateral-acceleration-based vehicle-models-blending for automated driving controllers
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2020-10-13
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Abstract
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.
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Publisher Copyright: © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
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Automated driving , Model predictive control , Trajectory tracking , Vehicle control , Vehicle-model blending , Control and Systems Engineering , Signal Processing , Hardware and Architecture , Computer Networks and Communications , Electrical and Electronic Engineering , SDG 11 - Sustainable Cities and Communities , Project ID , info: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/AUTODRIVE , info: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/AUTODRIVE , Funding Info , This 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. 737469 , This 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. 737469
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Matute-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/electronics9101674