Developments in cooperative trajectory planning of connected autonomous vehicles (CAVs) have gathered considerable momentum and research attention. Generally, such problems present strong non-linearity and non-convexity, rendering great difficulties in finding the optimal solution. Existing methods typically suffer from low computational efficiency, and this hinders the appropriate applications in large-scale scenarios involving an increasing number of vehicles. To tackle this problem, we propose a novel decentralized iterative linear quadratic regulator (iLQR) algorithm by leveraging the dual consensus alternating direction method of multipliers (ADMM). First, the original non-convex optimization problem is reformulated into a series of convex optimization problems through iterative neighbourhood approximation. Then, the dual of each convex optimization problem is shown to have a consensus structure, which facilitates the use of consensus ADMM to solve for the dual solution in a fully decentralized and parallel architecture. Finally, the primal solution corresponding to the trajectory of each vehicle is recovered by solving a linear quadratic regulator (LQR) problem iteratively, and a novel trajectory update strategy is proposed to ensure the dynamic feasibility of vehicles. With the proposed development, the computation burden is significantly alleviated such that real-time performance is attainable. Two traffic scenarios are presented to validate the proposed algorithm, and thorough comparisons between our proposed method and baseline methods (including centralized iLQR, IPOPT, and SQP) are conducted to demonstrate the scalability of the proposed approach.
翻译:为解决这一问题,我们建议采用新的分散式的、分散式的、线性线性梯度调节器(iLQR)算法,利用双共识的乘数交错方向法(LQPP)问题反复地解决,从而恢复与每一车辆轨迹相对轨迹相对的原始解决办法。 首先,最初的非convex优化问题通过迭接邻居近近近相,重订为一系列螺旋优化问题;然后,每个convex优化问题的双轨制问题显示出一个共识结构,这有利于在涉及数量越来越多的车辆的大规模假设中采用AdmMMD解决双重解决办法。最后,为了解决这一问题,我们建议采用新的分散式的、分散式的、分散式的双向线性线性线性线调调调控管(iLQPP)调控法,并提出了新的轨迹更新战略,以确保车辆的动态可行性。 随着拟议的发展、计算方法的升级,计算负担将大大降低SBR的进度。