This presentation provides an innovative distributed longitudinal control strategy for connected automated vehicles (CAVs) in the mixed traffic environment of CAV and human-driven vehicles (HDVs), incorporating high-dimensional platoon information. For mixed traffic, the traditional CAV control method focuses on microscopic trajectory information, which may not be efficient in handling the HDV stochasticity (e.g., long reaction time; various driving styles) and mixed traffic heterogeneities. Different from traditional methods, our method, for the first time, characterizes consecutive HDVs as a whole (i.e., AHDV) to reduce the HDV stochasticity and utilize its macroscopic features to control the following CAVs. The new control strategy takes advantage of platoon information to anticipate the disturbances and traffic features induced downstream under mixed traffic scenarios and greatly outperforms the traditional methods. In particular, the control algorithm is based on deep reinforcement learning (DRL) to fulfill car-following control efficiency and further address the stochasticity for the aggregated car-following behavior by embedding it in the training environment. To better utilize the macroscopic traffic features, a general platoon of mixed traffic is categorized as a CAV-HDVs-CAV pattern and described by corresponding DRL states. The macroscopic traffic flow properties are built upon the Newell car-following model to capture the characteristics of aggregated HDVs’ joint behaviors. Simulated experiments are conducted to validate our proposed strategy. The results demonstrate that the proposed control method has outstanding performances in terms of oscillation dampening, eco-driving, and generalization capability.
Yang Zhou, PhD, earned his Ph.D. from the University of Wisconsin-Madison and his master’s degree from the University of Illinois at Urbana-Champaign. He currently serves as an assistant professor in the Zachry Department of Civil and Environmental Engineering at Texas A&M University. In 2023, he was honored as the Career Initiation Fellow by the Texas A&M Institute of Data Science.
Dr. Zhou specializes in connected automated vehicle control, traffic flow analysis, AI applications in transportation, and high-fidelity simulations. Before joining Texas A&M, he served as a research associate, supported by both the Department of Civil and Environmental Engineering and the Department of Industry and System. Dr. Zhou has published more than 40 articles in top-tier transportation journals, including Transportation Research Part B, Transportation Research Part C, and IEEE Transactions on Intelligent Transportation Systems. Dr. Zhou is a committee member of the Transportation Research Board’s Traffic Flow Theory Committee, the American Society of Civil Engineering Safety and TDI-AI Committees, and the Editorial Board for Transportation Research Part C.