dc.contributor.author | Matute-Peaspan, Jose A. | |
dc.contributor.author | Marcano, Mauricio | |
dc.contributor.author | Diaz, Sergio | |
dc.contributor.author | Zubizarreta, Asier | |
dc.contributor.author | Perez, Joshue | |
dc.date.accessioned | 2020-10-22T10:19:36Z | |
dc.date.available | 2020-10-22T10:19:36Z | |
dc.date.issued | 2020-10-13 | |
dc.identifier.citation | Matute-Peaspan, Jose A., Mauricio Marcano, Sergio Diaz, Asier Zubizarreta, and Joshue Perez. “Lateral-Acceleration-Based Vehicle-Models-Blending for Automated Driving Controllers.” Electronics 9, no. 10 (October 13, 2020): 1674 | en |
dc.identifier.uri | http://hdl.handle.net/11556/1005 | |
dc.description.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 | en |
dc.description.sponsorship | 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 | en |
dc.language.iso | eng | en |
dc.publisher | Multidisciplinary Digital Publishing Institute (MDPI) | en |
dc.rights | Attribution 4.0 International | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.title | Lateral-Acceleration-Based Vehicle-Models-Blending for Automated Driving Controllers | en |
dc.type | journal article | en |
dc.identifier.doi | 10.3390/electronics9101674 | en |
dc.relation.projectID | 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 | en |
dc.rights.accessRights | open access | en |
dc.subject.keywords | Vehicle-model blending | en |
dc.subject.keywords | Trajectory tracking | en |
dc.subject.keywords | Model predictive control | en |
dc.subject.keywords | Automated driving | en |
dc.subject.keywords | Vehicle control | en |
dc.identifier.essn | 2079-9292 | en |
dc.issue.number | 10 | en |
dc.journal.title | Electronics | en |
dc.page.initial | 1674 | en |
dc.volume.number | 9 | en |