Pareto Optimal Information Flow Topology for Control of Connected Autonomous Vehicles

Information flow topology plays a crucial role in the control of connected autonomous vehicles. This paper proposes an approach to search for the Pareto optimal information flow topology off-line for the control of connected vehicles' platoon using a non-dominated sorting genetic algorithm. Based on the obtained Pareto optimal information flow topology, the platoon's overall performance in terms of three main performance indices: tracking index, acceleration standard deviation, and fuel consumption, are all improved. Numerical simulations are used to validate the effectiveness of the proposed approach. In the simulation, the impact of different information flow topologies on the performance of the connected autonomous vehicles platoon is firstly investigated. The results show that more communication links can lead to better tracking ability. The smoothness of the velocity profile is consistent with fuel economy, while velocity profile's smoothness, fuel economy and communication efficiency are in contrary to the tracking index. Then, five cases are discussed using the Pareto optimal information flow topology. The results indicate that while ensuring the platoon's stability, the obtained Pareto optimal information flow topology can improve the tracking ability by 33.67% to 49.35%, and fuel economy by 7.181% to 16.93% and driving comfort up to 14.9%.