This paper investigates a cooperative motion planning problem for large-scale connected autonomous vehicles (CAVs) under limited communications, which addresses the challenges of high communication and computing resource requirements. Our proposed methodology incorporates a parallel optimization algorithm with improved consensus ADMM considering a more realistic locally connected topology network, and time complexity of O(N) is achieved by exploiting the sparsity in the dual update process. To further enhance the computational efficiency, we employ a lightweight evolution strategy for the dynamic connectivity graph of CAVs, and each sub-problem split from the consensus ADMM only requires managing a small group of CAVs. The proposed method implemented with the receding horizon scheme is validated thoroughly, and comparisons with existing numerical solvers and approaches demonstrate the efficiency of our proposed algorithm. Also, simulations on large-scale cooperative driving tasks involving 80 vehicles are performed in the high-fidelity CARLA simulator, which highlights the remarkable computational efficiency, scalability, and effectiveness of our proposed development. Demonstration videos are available at https://henryhcliu.github.io/icadmm_cmp_carla.

翻译：本文研究在有限通信条件下的大规模网联自动驾驶车辆协同运动规划问题，旨在应对高通信与计算资源需求的挑战。所提方法结合了并行优化算法与改进型共识ADMM，考虑更贴近实际的局部连通拓扑网络，并通过利用对偶更新过程中的稀疏性实现O(N)时间复杂度。为进一步提升计算效率，我们对CAVs动态连通图采用轻量化演化策略，且从共识ADMM中分解出的每个子问题仅需管理少量CAVs。所提方法结合滚动时域方案进行充分验证，并与现有数值求解器及方法对比，证明了算法的高效性。此外，在CARLA高保真仿真器中开展涉及80辆车辆的大规模协同驾驶任务仿真，突出展示了所提方法卓越的计算效率、可扩展性与有效性。演示视频见https://henryhcliu.github.io/icadmm_cmp_carla。