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

Abstract

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Performance Analysis of Embedded Platoon Controllers

“…The lower layer controller is a state-feedback controller with a sampling rate of 2ms. The output of this controller is the motor duty cycle which controls the vehicle acceleration [2]. The upper-layer controller uses Model Predictive Controller (MPC) [19] with a 100ms sampling period, matching the ETSI-ITS communication standard maximum message rate.…”

“…For this paper we implemented the control strategy proposed in [2], a distributed multi-rate control strategy with an upper layer using an MPC controller and a lower layer using a statefeedback controller, on the Cohda Wireless MK5 embedded platform, developed by Cohda Wireless and NXP. We have developed an evaluation setup with four devices communicating wirelessly using the ETSI-ITS protocol, with the aim to explore the challenges that arise from the implementation and show that the theoretical performance of the approach also translates to a good performance in a real system.…”

Control of Platooned Vehicles in Presence of Traffic Shock Waves

“…In order to use MPC, the platoon model in Eq. 2 has to be discretized using Zero-Order Hold (ZOH) (refer to [10] for more details). After discretization, the predictive model for vehicle i can be obtained as shown in Eq.…”

“…Our approach is tested using CReTS toolchain [10] (ContRol, nEtwork and Traffic Simulator), which is a cosimulation framework that connects Matlab (for control design), the traffic simulator SUMO (for generating real driver behavior) [11], the network simulator ns-3 (for simulating V2V communication) (see Figure 1). Using microscopic simulations in SUMO, we compare the fuel consumption of platooned vehicles and show that preemptively lowering their speed leads to better fuel economy.…”

Holistic simulation for integrated vehicle design

“…stability under the proposed design and strategy to that under a baseline conventional ESP system. A. Ibrahim[39] developed a co-simulation framework between Matlab and SUMO and connected a Matlab multi-layer vehicle platooning control algorithm to a SUMO traffic behaviour model to evaluate the performance of the platooning control strategy. R. Bours et al[40] setup a co-simulation between of ADAS sensors and world simulation running in PreScan software and control algorithm and vehicle dynamics simulation running on Simulink using a dedicated co-simulation interface with the purpose of evaluating the sensitivity of the modelled Autonomous Emergency Braking (AEB) system via parameter variation.N.…”