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dc.contributor.authorMatute, Jose A.
dc.contributor.authorMarcano, Mauricio
dc.contributor.authorZubizarreta, Asier
dc.contributor.authorPerez, Joshue
dc.date.accessioned2018-06-14T12:29:49Z
dc.date.available2018-06-14T12:29:49Z
dc.date.issued2018-06
dc.identifier.citationMatute, Jose A., Mauricio Marcano, Asier Zubizarreta, and Joshue Perez. “Longitudinal Model Predictive Control with Comfortable Speed Planner.” 2018 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC) (April 2018) 60 - 64. doi:10.1109/icarsc.2018.8374161en
dc.identifier.isbn978-1-5386-5222-0en
dc.identifier.urihttp://hdl.handle.net/11556/578
dc.description.abstractGuaranteeing simplicity and safety is a real challenge of Advanced Driver Assistance Systems (ADAS), being these aspects necessary for the development of decision and control stages in highly automated vehicles. Considering that a human-centered design is generally pursued, exploring comfort boundaries in passenger vehicles has a significant importance. This work aims to implement a simple Model Predictive Control (MPC) for longitudinal maneuvers, considering a bare speed planner based on the curvature of a predefined geometrical path. The speed profiles are constrained with a maximum value at any time, in such way that total accelerations are lower than specified constraint limits. A double proportional with curvature bias control was employed as a simple algorithm for lateral maneuvers. The tests were performed within a realistic simulation environment with a virtual vehicle model based on a multi-body formulation. The results of this investigation permits to determine the capabilities of simplified control algorithms in real scenarios, and comprehend how to improve them to be more efficient.en
dc.description.sponsorshipAuthors want to acknowledge their organization. This project has received funding from the Electronic Component Systems for European Leadership Joint Undertaking under grant agreement No 737469 (AutoDrive Project). This Joint Undertaking receives support from the European Unions Horizon 2020 research and innovation programme and Germany, Austria, Spain, Italy, Latvia, Belgium, Netherlands, Sweden, Finland, Lithuania, Czech Republic, Romania, Norway. This work was developed at Tecnalia Research & Innovation facilities supporting this research.en
dc.language.isoengen
dc.publisherIEEEen
dc.titleLongitudinal Model Predictive Control with comfortable speed planneren
dc.typeconferenceObjecten
dc.identifier.doi10.1109/icarsc.2018.8374161en
dc.relation.projectIDinfo: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/AUTODRIVEen
dc.rights.accessRightsopenAccessen
dc.subject.keywordsModel Predictive Controlen
dc.subject.keywordsSimulation Environmenten
dc.subject.keywordsAutomated Drivingen
dc.page.final64en
dc.page.initial60en
dc.identifier.esbn978-1-5386-5221-3en
dc.conference.title2018 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC) 25-27 April 2018, Torres Vedras, Portugal.en


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