Longitudinal Model Predictive Control with comfortable speed planner
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2018-06-06
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IEEE
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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.
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Publisher Copyright: © 2018 IEEE.
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Model Predictive Control , Simulation Environment , Automated Driving , Model Predictive Control , Simulation Environment , Automated Driving , Artificial Intelligence , Mechanical Engineering , Control and Optimization , 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 , 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. , 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.
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Matute , 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.2018.8374161
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