Scalable Platooning Based on Directed Information Flow Topology With Granulating Method

Abstract

Vehicle platooning is of great importance in future autonomous driving and intelligent transportation systems, due to its advantages in road safety, traffic efficiency, energy consumption and exhaust emissions. This paper focuses on the scalability performance of platooning control, which aims to achieve long platoon size under the premise of ensuring consensus behavior of the platooning vehicles. However, in classical platooning schemes such as ACC (adaptive cruise control) and CACC (cooperative adaptive cruise control), as the number of platoon members increases, the communication range of the leader and the cascaded sensor delay affect the scalability of platoon. In this paper, a scalable platooning scheme, CACC-granulation, is proposed to improve the scalability of platooning based on a novel information flow topology. The granulating method is used to solve the problem of limited communication range of leader for CACC by forwarding their own information to platoon members through some vehicles. The CACC-granulation granulates platoon information flow topology and enhances the platoon scalability by reducing information flow topology matrix. Simulation experiments are conducted to verify the consensus and scalability performance of CACC-granulation. Compared with other two platooning schemes which can get long platoon size, i.e., ACC-cascade and ACC-CACC-integration, the simulation results indicate the performance advantages of the proposed CACC-granulation, which not only meets the consensus of platooning control, but also enhances the scalability of platooning control.

Keywords

• Platoon
• consensus
• scalability
• ACC (adaptive cruise control)
• CACC (cooperative adaptive cruise control)
• granulating method