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This paper investigates a cooperative motion planning problem for large-scaleconnected autonomous vehicles (CAVs) under limited communications, whichaddresses the challenges of high communication and computing resourcerequirements. Our proposed methodology incorporates a parallel optimizationalgorithm with improved consensus ADMM considering a more realistic locallyconnected topology network, and time complexity of O(N) is achieved byexploiting the sparsity in the dual update process. To further enhance thecomputational efficiency, we employ a lightweight evolution strategy for thedynamic connectivity graph of CAVs, and each sub-problem split from theconsensus ADMM only requires managing a small group of CAVs. The proposedmethod implemented with the receding horizon scheme is validated thoroughly,and comparisons with existing numerical solvers and approaches demonstrate theefficiency of our proposed algorithm. Also, simulations on large-scalecooperative driving tasks involving 80 vehicles are performed in thehigh-fidelity CARLA simulator, which highlights the remarkable computationalefficiency, scalability, and effectiveness of our proposed development.Demonstration videos are available athttps://henryhcliu.github.io/icadmm_cmp_carla.

Improved Consensus ADMM for Cooperative Motion Planning of Large-Scale Connected Autonomous Vehicles with Limited Communication