Feasibility study and benchmarking of embedded MPC for vehicle platoons

Iñaki Martín Soroa, Amr Ibrahim, Dip Goswami, Hong Li

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

1 Citation (Scopus)
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This paper performs a feasibility analysis of deploying Model Predictive Control (MPC) for vehicle platooning on an On-Board Unit (OBU) and performance benchmarking considering interference from other (system) tasks running on an OBU. MPC is a control strategy that solves an implicit (on-line) or explicit (off-line) optimisation problem for computing the control input in every sample. OBUs have limited computational resources. The challenge is to implement an MPC algorithm on such automotive Electronic Control Units (ECUs) with an acceptable timing behavior. Moreover, we should be able to stop the execution if necessary at the cost of performance. We measured the computational capability of a unit developed by Cohda Wireless and NXP under the influence of its Operating System (OS). Next, we analysed the computational requirements of different state-of-the-art MPC algorithms by estimating their execution times. We use off-the-shelf and free automatic code generators for MPC to run a number of relevant MPC algorithms on the platform. From the results, we conclude that it is feasible to implement MPC on automotive ECUs for vehicle platooning and we further benchmark their performance in terms of MPC parameters such as prediction horizon and system dimension.

Original languageEnglish
Title of host publicationWorkshop on Autonomous Systems Design (ASD 2019)
EditorsSelma Saidi, Rolf Ernst, Dirk Ziegenbein
Place of PublicationWadern
PublisherSchloss Dagstuhl - Leibniz-Zentrum für Informatik
Number of pages15
ISBN (Electronic)978-3-95977-102-3
Publication statusPublished - 1 Mar 2019
Event1st International Workshop on Autonomous Systems Design, ASD 2019 - Florence, Italy
Duration: 29 Mar 201929 Mar 2019

Publication series

NameOpenAccess Series in Informatics
PublisherCurran Associates
ISSN (Print)2190-6807


Conference1st International Workshop on Autonomous Systems Design, ASD 2019


  • Code generation
  • Embedded implementation
  • Model predictive control
  • Vehicle platoon


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