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Development of model predictive motion planning and control for autonomous vehicles

  • Guido TosolinEmail author
  • Jaume Cartró
  • Vishwas Sharma
Conference paper
Part of the Proceedings book series (PROCEE)

Abstract

Full autonomous vehicles for the general public are getting closer every year. Among all the challenges to overcome, one of them is the acceptance of this technology which translates to make the passengers enjoy being driven. To achieve this objective, automated vehicles will have to focus on performance attributes such as comfort, stability or efficiency and vehicles dynamics development will take care of it.

At Idiada, research about this topic is being carried out and strategies about motion planning and control (path follower) will be proposed in this paper. The development is based on optimal control method like Model Predictive Control (MPC).

Among all the possibilities to face the problem, MPC was chosen for several reasons. MPC allows setting constraints in our control inputs like maximum steering wheel angle or vehicle states like accelerations. However, the main reason to use MPC is its way of planning in advance control actions that behaves very similar to how a human driver would do. This feature is key in our understanding to make autonomous vehicles be accepted by all passengers.

Also, our contribution yields in finding the correct vehicle dynamics metrics to design and adjust all the cost functions.

Finally, thanks to our new acquisition, the DIM 250 VI-Grade Simulator which is able to reproduce up to 2.5G accelerations, all our development will be evaluated in a fast and secure testing environment.

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References

  1. [1] ‘Search-based optimal motion planning for automated driving’, Zlatan Ajanovic1, Bakir Lacevic, Barys Shyrokau, Michael Stolz and Martin Horn, August 2018Google Scholar
  2. [2] ‘Real-time Motion Planning with Applications to Autonomous Urban Driving’, Yoshiaki Kuwata, Gaston Fiore, Emilio Frazzoli, September 2009Google Scholar
  3. [3] ‘Autonomous racing using Linear Parameter Varying -Model Predictive Control (LPV-MPC)’, Eugenio Alcalá, Vicenç Puig, Joseba Quevedo, Ugo RosoliaGoogle Scholar

Copyright information

© Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Applus IDIADA GroupSanta OlivaSpain

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