Cooperative Platooning and Lane Changing for Connected and Automated Vehicles: An Entropy-Based Method

Abstract

Connected and Automated Vehicles (CAV) have accurate distance sensing which makes shorter headway and reduced shockwave possible, and eventually increase the roadway throughput or capacity. The vehicle to vehicle (V2V) communication equipped on CAV allows vehicles to exchange information, and form platoons more efficiently. This paper uses the Intelligent Driver Model (IDM) as the behavior model to simulate CAVs in mixed traffic conditions with both CAVs and Human Driven Vehicles (HDV) under different CAV penetration rates. A cooperative CAV platooning method is also introduced to guide CAVs surrounded by HDVs into CAV platoons, Partial Autonomous vehicle Lane change (PAL) is used in low CAV percentage while Full Autonomous vehicle Lane change (FAL) is used in high CAV percentage. Block Entropy is then used as a measurement for platooning performance. The result shows that capacity will increase as CAV percentage grows, and the peak growths are in medium CAV percentage between 40% and 70%. The cooperative CAV platooning algorithm are found to decrease HDV-CAV conflicts as well as have some marginal improvement to capacity under all CAV percentages. The simulation performance suggests that the threshold for using FAL or PAL is 55%. Furthermore, block entropy is proved to measure CAV platooning performance efficiently and represent capacity change to some extent.