Strategy of lane-changing coupling process for connected and automated vehicles in mixed traffic environment

Heterogeneous traffic agents consisting of human-driven vehicle (HDV) and connected-automated vehicle (CAV) appear on the road and form a mixed traffic environment. When combining the characteristics of CAVs and HDVs, it's still an open issue of investigating how heterogeneous traffic agents will execute lane-changing. With respect to the coupling process in the lane-changing behaviour of vehicles, this paper derives the lane changing optimization method for CAVs under different penetration rates. This method captures not only the full spectrum of CAV penetration rates but also all types of vehicle platoon. Firstly, the different car-following and lane-changing models are used to capture the characteristics of different types of vehicles. Moreover, For CAV, the Cooperative Adaptive Cruise Control (CACC) vehicle would degrade into the Adaptive Cruise Control (ACC) vehicle due to communication failure. Secondly, the framework for the coupling analysis of CAV lane-changing behaviour and mixed traffic stability is derived. Then, this study proposes and examines optimal control strategies based on numerical simulation. These consist of a longitudinal phase, in which the proposed strategies improve the operational efficiency of the overall mixed traffic flow, and a lateral phase, in which they safely change lanes. The results show that they improve the comfort, efficiency, safety, and stability of the heterogeneous traffic agents compared to the maneuvers performed by the baseline. This method can serve as a useful and simple decision tool for future CAV lane management.

Automated summary

This paper investigates the lane-changing behavior of heterogeneous traffic agents consisting of human-driven vehicles (HDVs) and connected-automated vehicles (CAVs). It proposes an optimization method for CAV lane changing under different penetration rates and examines the effectiveness of optimal control strategies through numerical simulations. The results show that these strategies improve the comfort, efficiency, safety, and stability of heterogeneous traffic agents compared to baseline maneuvers.