论文标题
使用社会价值取向混合交通中的连接和自动化车辆的合作最佳控制框架
A Cooperative Optimal Control Framework for Connected and Automated Vehicles in Mixed Traffic Using Social Value Orientation
论文作者
论文摘要
在本文中，我们开发了一个具有社会合作的最佳控制框架，以使用社会价值取向（SVO）（SVO）和潜在的游戏方法解决混合流量中的连接和自动化车辆（CAVS）的运动计划问题。在拟议的框架中，我们制定了CAV与人类驱动车辆（HDV）之间的相互作用，作为同时游戏，在该游戏中，每辆车都可以最大程度地减少其利己主义目标的加权和合作目标。 SVO角度用于量化车辆对利己主义和合作目标的偏好。使用潜在的游戏方法，我们为最佳控制问题提出了一个单一的目标函数，其加权因素是根据车辆的SVOS选择的。我们证明，可以通过最大程度地减少提出的目标函数来获得NASH平衡。为了估计HDV的SVO角度，我们基于最大熵逆增强学习而开发了一种移动范围估计算法。通过对车辆合并方案的数值模拟，提出的方法的有效性证明了。
In this paper, we develop a socially cooperative optimal control framework to address the motion planning problem for connected and automated vehicles (CAVs) in mixed traffic using social value orientation (SVO) and a potential game approach. In the proposed framework, we formulate the interaction between a CAV and a human-driven vehicle (HDV) as a simultaneous game where each vehicle minimizes a weighted sum of its egoistic objective and a cooperative objective. The SVO angles are used to quantify preferences of the vehicles toward the egoistic and cooperative objectives. Using the potential game approach, we propose a single objective function for the optimal control problem whose weighting factors are chosen based on the SVOs of the vehicles. We prove that a Nash equilibrium can be obtained by minimizing the proposed objective function. To estimate the SVO angle of the HDV, we develop a moving horizon estimation algorithm based on maximum entropy inverse reinforcement learning. The effectiveness of the proposed approach is demonstrated by numerical simulations of a vehicle merging scenario.
