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Feasibility Study and Benchmarking of Embedded MPC for Vehicle Platoons

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

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Author Details

Iñaki Martín Soroa
  • Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
Amr Ibrahim
  • Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
Dip Goswami
  • Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
Hong Li
  • Car Infotainment and Driving Assistance, NXP Semiconductor, Eindhoven, The Netherlands

Funding

This work is partially supported by the H2020 projects I-MECH (GA no. 737453) and oCPS (GA no. 674875).

Cite As Get BibTex

Iñaki Martín Soroa, Amr Ibrahim, Dip Goswami, and Hong Li. Feasibility Study and Benchmarking of Embedded MPC for Vehicle Platoons. In Workshop on Autonomous Systems Design (ASD 2019). Open Access Series in Informatics (OASIcs), Volume 68, pp. 2:1-2:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019) https://doi.org/10.4230/OASIcs.ASD.2019.2

Abstract

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.

Subject Classification

ACM Subject Classification
  • Applied computing → Command and control
  • Computer systems organization → Embedded systems
Keywords
  • Model predictive control
  • vehicle platoon
  • embedded implementation
  • code generation

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