Matute, Jose A.Marcano, MauricioZubizarreta, AsierPerez, JoshueCalado, JoaoBento, Luis CondeOliveira, PauloCostelha, HugoLopes, Nuno2018-06-06Matute , J A , Marcano , M , Zubizarreta , A & Perez , J 2018 , Longitudinal Model Predictive Control with comfortable speed planner . in J Calado , L C Bento , P Oliveira , H Costelha & N Lopes (eds) , unknown . 18th IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2018 , IEEE , pp. 60-64 , 18th IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2018 , Torres Vedras , Portugal , 25/04/18 . https://doi.org/10.1109/icarsc.2018.8374161conference978-1-5386-5222-0978-1-5386-5221-39781538652213researchoutputwizard: 11556/578Publisher Copyright: © 2018 IEEE.Guaranteeing 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.5261235enginfo:eu-repo/semantics/openAccessconference output10.1109/icarsc.2018.8374161Model Predictive ControlSimulation EnvironmentAutomated DrivingModel Predictive ControlSimulation EnvironmentAutomated DrivingArtificial IntelligenceMechanical EngineeringControl and OptimizationProject 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 InfoAuthors want to acknowledge their organization. This project_x000D_ has received funding from the Electronic Component Systems_x000D_ for European Leadership Joint Undertaking under grant agreement_x000D_ No 737469 (AutoDrive Project). This Joint Undertaking_x000D_ receives support from the European Unions Horizon 2020_x000D_ research and innovation programme and Germany, Austria, Spain, Italy, Latvia, Belgium, Netherlands, Sweden, Finland,_x000D_ Lithuania, Czech Republic, Romania, Norway. This work_x000D_ was developed at Tecnalia Research & Innovation facilities_x000D_ supporting this research.Authors want to acknowledge their organization. This project_x000D_ has received funding from the Electronic Component Systems_x000D_ for European Leadership Joint Undertaking under grant agreement_x000D_ No 737469 (AutoDrive Project). This Joint Undertaking_x000D_ receives support from the European Unions Horizon 2020_x000D_ research and innovation programme and Germany, Austria, Spain, Italy, Latvia, Belgium, Netherlands, Sweden, Finland,_x000D_ Lithuania, Czech Republic, Romania, Norway. This work_x000D_ was developed at Tecnalia Research & Innovation facilities_x000D_ supporting this research.http://www.scopus.com/inward/record.url?scp=85048897008&partnerID=8YFLogxK