The development of connected autonomous vehicles (CAVs) facilitates the enhancement of traffic efficiency in complicated scenarios. In unsignalized roundabout scenarios, difficulties remain unsolved in developing an effective and efficient coordination strategy for CAVs. In this paper, we formulate the cooperative autonomous driving problem of CAVs in the roundabout scenario as a constrained optimal control problem, and propose a computationally-efficient parallel optimization framework to generate strategies for CAVs such that the travel efficiency is improved with hard safety guarantees. All constraints involved in the roundabout scenario are addressed appropriately with convex approximation, such that the convexity property of the reformulated optimization problem is exhibited. Then, a parallel optimization algorithm is presented to solve the reformulated optimization problem, where an embodied iterative nearest neighbor search strategy to determine the optimal passing sequence in the roundabout scenario. It is noteworthy that the travel efficiency in the roundabout scenario is enhanced and the computation burden is considerably alleviated with the innovation development. We also examine the proposed method in CARLA simulator and perform thorough comparisons with a rule-based baseline and the commonly used IPOPT optimization solver to demonstrate the effectiveness and efficiency of the proposed approach.

翻译：联网自主车辆（CAVs）的发展促进了复杂场景下交通效率的提升。在无信号环岛场景中，如何制定高效且有效的CAVs协同策略仍存在未解难题。本文将环岛场景中CAVs的协同自动驾驶问题构建为带约束的最优控制问题，提出一种计算高效的并行优化框架，在确保硬安全约束的前提下生成提升通行效率的CAVs策略。通过凸近似方法合理处理环岛场景中的所有约束条件，使重构后的优化问题呈现凸性特征。随后提出并行优化算法求解该重构问题，其中嵌入迭代最近邻搜索策略以确定环岛场景中的最优通行序列。值得注意的是，该创新方法显著提升了环岛场景的通行效率并大幅降低了计算负担。我们在CARLA仿真器中验证了所提方法，并与基于规则的基准方法及常用的IPOPT优化求解器进行了全面对比，证明了该方法的有效性和高效性。