A Speed Planner Approach Based On Bézier Curves Using Vehicle Dynamic Constrains and Passengers Comfort

Ray Lattarulo, Enrique Marti, Mauricio Marcano, Jose Matute, Joshue Perez

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Citations (Scopus)
1 Downloads (Pure)

Abstract

This paper presents a speed profile generation approach for longitudinal control of automated vehicles, based on quintic Bézier curves. The described method aims to increase comfort level of passengers based on the ISO2631-1 specification, while taking into account vehicle dynamics and traffic rules to keep high safety levels. The algorithm has been tested in an in-house tool for high accuracy vehicle dynamics simulations, called Dynacar. The considered scenario is a closed circuit inside Tecnalia facilities. The resulting profile has better properties (for example, rate of change) than a raw input based on traffic speed limits. When used as reference for the speed controller, it improves both comfort and safety.
Original languageEnglish
Title of host publicationunknown
PublisherIEEE
Pages1-5
Number of pages5
ISBN (Electronic)978-1-5386-4881-0, 9781538648810
ISBN (Print)978-1-5386-4882-7
DOIs
Publication statusPublished - May 2018
Event2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018 - Florence, Italy
Duration: 27 May 201830 May 2018

Publication series

Name0271-4310

Conference

Conference2018 IEEE International Symposium on Circuits and Systems, ISCAS 2018
Country/TerritoryItaly
CityFlorence
Period27/05/1830/05/18

Keywords

  • Intelligent Transportation Systems
  • Automated Driving
  • Motion Planning
  • Longitudinal Control
  • Speed Profile

Project and Funding Information

  • Project ID
  • info:eu-repo/grantAgreement/EC/H2020/643921/EU/Unifying Control and Verification of Cyber-Physical Systems/UNCOVERCPS
  • info:eu-repo/grantAgreement/EC/H2020/769944/EU/ Smart-Taylored L-category Electric Vehicle demonstration in hEtherogeneous urbanuse-cases/ STEVE
  • Funding Info
  • This work is partly supported by the H2020 project_x000D_ UnCoVerCPS with grant number 643921, and the H2020_x000D_ Project STEVE with proposal identification number_x000D_ 769944.

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