基于半定松弛的ADMM互联自主车辆协同规划与控制
Semi-Definite Relaxation Based ADMM for Cooperative Planning and Control of Connected Autonomous Vehicles
摘要：
研究了不同场景下多辆互联自主车的协同规划与控制问题。在现有的文献中，大多数方法在计算效率方面存在显著的问题。此外，由于优化问题是非线性和非凸的，它通常构成了很大的困难，在确定最优解。为了解决这个问题，这项工作提出了一种新的和完全并行的计算框架，利用交替方向乘法器（ADMM）。自动驾驶问题中的非线性和非凸优化问题可以划分为两个可管理的子问题;并且由此产生的子问题可以在并行框架中使用有效的优化方法来求解。在这里，微分动态规划（DDP）算法是能够解决系统动力学的非线性，而有效的和非凸耦合约束的小尺寸可以近似调用的概念半定松弛（SDR），这也可以在很短的时间内解决。由于并行计算和非凸约束的有效松弛，我们提出的方法有效地实现了实时实现，从而也提供了额外的驾驶安全保证。最后，通过两个多CAV运输场景的仿真验证了该方法的有效性。
英文摘要：
This paper investigates the cooperative planning and control problem for multiple connected autonomous vehicles (CAVs) in different scenarios. In the existing literature, most of the methods suffer from significant problems in computational efficiency. Besides, as the optimization problem is nonlinear and nonconvex, it typically poses great difficultly in determining the optimal solution. To address this issue, this work proposes a novel and completely parallel computation framework by leveraging the alternating direction method of multipliers (ADMM). The nonlinear and nonconvex optimization problem in the autonomous driving problem can be divided into two manageable subproblems; and the resulting subproblems can be solved by using effective optimization methods in a parallel framework. Here, the differential dynamic programming (DDP) algorithm is capable of addressing the nonlinearity of the system dynamics rather effectively; and the nonconvex coupling constraints with small dimensions can be approximated by invoking the notion of semi-definite relaxation (SDR), which can also be solved in a very short time. Due to the parallel computation and efficient relaxation of nonconvex constraints, our proposed approach effectively realizes real-time implementation and thus also extra assurance of driving safety is provided. In addition, two transportation scenarios for multiple CAVs are used to illustrate the effectiveness and efficiency of the proposed method.
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