Co-simulation framework for control, communication and traffic for vehicle platoons

Ibrahim, A.; Math, C.B.; Goswami, D.; Basten, A.A.; Li, H.

doi:10.1109/DSD.2018.00068

Co-simulation framework for control, communication and traffic for vehicle platoons

Title

Co-simulation framework for control, communication and traffic for vehicle platoons

Author

Ibrahim, A.
Math, C.B.
Goswami, D.
Basten, A.A.
Li, H.

Contributor

Konofaos, N. (editor)
Novotny, M. (editor)
Skavhaug, A. (editor)

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.

Subject

Multi-layer-control
Network-simulator
Traffic-simulator
V2V-communication
Vehicle-platoons
Compliance control
Engines
MATLAB
Model predictive control
Network layers
Predictive control systems
Systems analysis
Traffic congestion
Vehicle actuated signals
Vehicle transmissions
Wireless telecommunication systems
Engine control system
In-vehicle networking
Industrial standards
Network simulators
Traffic simulators
V2V communications
Vehicle platoons
Wireless communications
Vehicle to vehicle communications