An Efficient Distributed Optimization Algorithm for Cooperation of Automated Vehicles Considering Packet Loss

With the development of wireless communication technologies, cooperation of automated vehicles (CAVs) becomes a key roadmap to promote the intelligent level and traffic efficiency. In this study, a distributed optimization framework is firstly designed to utilize more computation resources by introducing auxiliary variables and equality constraints to separate the coupling parts in the original centralized optimization problem. Bench test results show that more resources can be used by this framework compared with the centralized one, which is beneficial to the real time performance and scale of CAVs. But extra exchanges of the consensus variables between nodes lead to much more communication load, which easily causes packet loss. To ensure the cooperative performance, a robust interactive algorithm is further designed to ensure the convergence of the numerical optimization process in the presence of packet loss. Its global convergence is analyzed theoretically by the operator method under the assumption that the feasible domain is convex. The performances of CAVs controlled by the robust distributed optimization algorithm are validated and verified by several comparative tests under the intersection scenario. The test results show that compared with the centralized structure, the balance of computation load among different nodes is improved by 5 times at least, and the maximum computation period is smaller than 50 ms.