A Cooperative Control Method for On-Ramp Merging Under Mixed Traffic Flow

In contemporary society, autonomous driving systems face enormous challenges in various aspects, including the environment, traffic participants, and communication. The primary task of achieving vehicle autonomy is to ensure that Connected and Automated Vehicles (CAVs) can cooperate safely and efficiently with Human-Driven Vehicles (HDVs). Previous research mainly focused on strategies for purely CAV scenarios or treated HDVs as random factors in traffic, with relatively few studies considering the synchronous inducement and control of both types of vehicle within a unified framework. This research focuses on mixed traffic flow that includes both HDVs and CAVs, specifically addressing the merging problem at highway on-ramp entrances. We propose an inducement control method and a planning framework that target both types of vehicles simultaneously. For upstream HDVs, speed inducement is implemented based on time series predictions, along with behavior modeling that includes both internal and external uncertainties using a Gaussian mixture model. For ramp vehicles, merging decisions are made while considering the uncertainties of the upstream vehicles. Furthermore, based on the speed inducement strategy and the decision module, a cooperative uncertainty-aware planning model is constructed to achieve motion planning. The research demonstrates that the proposed speed inducement strategy can improve traffic safety and driver experience, and the cooperative control framework designed for both types of vehicles exhibits real-time functionality and performs excellently in terms of computation speed and planning success rates.