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Connected and automated vehicle (CAV) technology is one of the promisingsolutions to addressing the safety, mobility and sustainability issues of ourcurrent transportation systems. Specifically, the control algorithm plays animportant role in a CAV system, since it executes the commands generated byformer steps, such as communication, perception, and planning. In this study,we propose a consensus algorithm to control the longitudinal motion of CAVs inreal time. Different from previous studies in this field where control gains ofthe consensus algorithm are pre-determined and fixed, we develop algorithms tobuild up a lookup table, searching for the ideal control gains with respect todifferent initial conditions of CAVs in real time. Numerical simulation showsthat, the proposed lookup table-based consensus algorithm outperforms theauthors' previous work, as well as van Arem's linear feedback-basedlongitudinal motion control algorithm in all four different scenarios withvarious initial conditions of CAVs, in terms of convergence time and maximumjerk of the simulation run.

Lookup Table-Based Consensus Algorithm for Real-Time Longitudinal Motion Control of Connected and Automated Vehicles