License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.ASD.2019.2
URN: urn:nbn:de:0030-drops-103359
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Martín Soroa, Iñaki ; Ibrahim, Amr ; Goswami, Dip ; Li, Hong

Feasibility Study and Benchmarking of Embedded MPC for Vehicle Platoons

OASIcs-ASD-2019-2.pdf (0.8 MB)


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.

BibTeX - Entry

  author =	{I{\~n}aki Mart{\'i}n Soroa and Amr Ibrahim and Dip Goswami and Hong Li},
  title =	{{Feasibility Study and Benchmarking of Embedded MPC for Vehicle Platoons}},
  booktitle =	{Workshop on Autonomous Systems Design (ASD 2019)},
  pages =	{2:1--2:15},
  series =	{OpenAccess Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-102-3},
  ISSN =	{2190-6807},
  year =	{2019},
  volume =	{68},
  editor =	{Selma Saidi and Rolf Ernst and Dirk Ziegenbein},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{},
  doi =		{10.4230/OASIcs.ASD.2019.2},
  annote =	{Keywords: Model predictive control, vehicle platoon, embedded implementation, code generation}

Keywords: Model predictive control, vehicle platoon, embedded implementation, code generation
Collection: Workshop on Autonomous Systems Design (ASD 2019)
Issue Date: 2019
Date of publication: 28.03.2019

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