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Co-simulation Framework for Control, Communication and Traffic for Vehicle Platoons

Authors :
Ibrahim, Amr
Math, Chetan Belagal
Goswami, Dip
Basten, Twan
Li, Hong
Konofaos, Nikos
Novotny, Martin
Skavhaug, Amund
Electronic Systems
Electro-Optical Communication
Cyber-Physical Systems Center Eindhoven
Embedded Control Systems Lab
CompSOC Lab- Predictable & Composable Embedded Systems
Source :
2018 21st Euromicro Conference on Digital System Design (DSD), DSD, Konofaos, N.Novotny, M.Skavhaug, A., Proceedings-21st Euromicro Conference on Digital System Design, DSD 2018, 29 August 2018 through 31 August 2018, 352-356, Proceedings-21st Euromicro Conference on Digital System Design, DSD 2018, 352-356, STARTPAGE=352;ENDPAGE=356;TITLE=Proceedings-21st Euromicro Conference on Digital System Design, DSD 2018
Publication Year :
2018

Abstract

Vehicle platooning has gained attention for its potential to achieve an increased road capacity and safety, and a higher fuel efficiency. Member vehicles of a platoon wirelessly communicate complying with industrial standards such as IEEE 802.11p. By exchanging information with other members via wireless communication, a platoon member computes its desired acceleration which is then passed on to the engine control system via in-vehicle network to physically realize the acceleration. This leads to a multi-layer control scheme. The upper-layer is influenced by the behavior of 802.11p communication and network congestion due to transmissions by other vehicles in the traffic. The lower-layer engine control loop communicates over the fast and reliable in-vehicle networks (e.g., FlexRay, Ethernet). Design of the overall system therefore depends on (i) the characteristics of 802.11p-based communication (ii) the nature of the traffic (iii) the control algorithms running at the two layers. We present a cosimulation framework consisting of Matlab (for the multi-layer control algorithms), ns-3 (for the 802.11p network) and SUMO (for the traffic behavior). The framework can be used to validate different platooning setups. As an illustrative case study, we consider a multi-layer control strategy where the upper-layer uses Model Predictive Control (MPC) at a rate in compliance with 802.11p and the lower-layer uses statefeedback control at a higher sampling rate in line with in-vehicle networking capabilities. The control strategy is evaluated considering various realistic traffic and network congestion scenarios. © 2018 IEEE.

Details

ISBN :
978-1-5386-7377-5
ISBNs :
9781538673775
Database :
OpenAIRE
Journal :
2018 21st Euromicro Conference on Digital System Design (DSD)
Accession number :
edsair.doi.dedup.....4a3123ad427f00d61f49410b7600a75d
Full Text :
https://doi.org/10.1109/dsd.2018.00068