Markov chain-based platoon recognition model in mixed traffic with human-driven and connected and autonomous vehicles

Abstract

Many vehicle platoons are interrupted while traveling on roads, especially at urban signalized intersections. One reason for such interruptions is the inability to exchange real-time information between traditional human-driven vehicles and intersection infrastructure. Thus, this paper develops a Markov chain-based model to recognize platoons. A simulation experiment is performed in Vissim based on field data extracted from video recordings to prove the model’s applicability. The videos, recorded with a high-definition camera, contain field driving data from three Tesla vehicles, which can achieve Level 2 autonomous driving. The simulation results show that the recognition rate exceeds 80% when the connected and autonomous vehicle penetration rate is higher than 0.7. Whether a vehicle is upstream or downstream of an intersection also affects the performance of platoon recognition. The platoon recognition model developed in this paper can be used as a signal control input at intersections to reduce the unnecessary interruption of vehicle platoons and improve traffic efficiency.

Keywords

mixed traffic / connected and autonomous vehicles / Markov chain / platoon recognition / Vissim simulation

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