论文标题
贝叶斯优化的混合交通中连接和自动化车辆的模型预测控制的最佳重量适应
Optimal Weight Adaptation of Model Predictive Control for Connected and Automated Vehicles in Mixed Traffic with Bayesian Optimization
论文作者
论文摘要
在本文中，我们为混合流量中的连接和自动化车辆（CAVS）制定了模型预测控制（MPC）的最佳权重适应策略。我们将CAV和人类驱动车辆（HDV）之间的相互作用建模为同时游戏，并制定游戏理论的MPC问题，以找到游戏的NASH平衡。在MPC问题中，可以使用移动视野逆增强学习在线学习HDV目标功能中的权重。使用贝叶斯优化，我们提出了一种策略，以最佳地调整CAV目标函数中的权重，以便可以最大程度地减少在模拟中使用MPC时的预期真实成本。我们通过在未信号的交叉点上对车辆交叉示例的数值模拟来验证最佳策略的有效性。
In this paper, we develop an optimal weight adaptation strategy of model predictive control (MPC) for connected and automated vehicles (CAVs) in mixed traffic. We model the interaction between a CAV and a human-driven vehicle (HDV) as a simultaneous game and formulate a game-theoretic MPC problem to find a Nash equilibrium of the game. In the MPC problem, the weights in the HDV's objective function can be learned online using moving horizon inverse reinforcement learning. Using Bayesian optimization, we propose a strategy to optimally adapt the weights in the CAV's objective function so that the expected true cost when using MPC in simulations can be minimized. We validate the effectiveness of the optimal strategy by numerical simulations of a vehicle crossing example at an unsignalized intersection.
