dc.contributor.author | Matute, Jose A. | |
dc.contributor.author | Marcano, Mauricio | |
dc.contributor.author | Zubizarreta, Asier | |
dc.contributor.author | Perez, Joshue | |
dc.date.accessioned | 2018-06-14T12:29:49Z | |
dc.date.available | 2018-06-14T12:29:49Z | |
dc.date.issued | 2018-06 | |
dc.identifier.citation | Matute, 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.2018.8374161 | en |
dc.identifier.isbn | 978-1-5386-5222-0 | en |
dc.identifier.uri | http://hdl.handle.net/11556/578 | |
dc.description.abstract | 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. | en |
dc.description.sponsorship | Authors 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.iso | eng | en |
dc.publisher | IEEE | en |
dc.title | Longitudinal Model Predictive Control with comfortable speed planner | en |
dc.type | conference output | en |
dc.identifier.doi | 10.1109/icarsc.2018.8374161 | 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 | Model Predictive Control | en |
dc.subject.keywords | Simulation Environment | en |
dc.subject.keywords | Automated Driving | en |
dc.page.final | 64 | en |
dc.page.initial | 60 | en |
dc.identifier.esbn | 978-1-5386-5221-3 | en |
dc.conference.title | 2018 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC) 25-27 April 2018, Torres Vedras, Portugal. | en |