Hybrid MPC System for Platoon based Cooperative Lane change Control Using Machine Learning Aided Distributed Optimization
Hybrid MPC System for Platoon based Cooperative Lane change Control Using Machine Learning Aided Distributed Optimization
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DOI：
10.1016/j.trb.2021.10.006
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
Hanyu Zhang;Lili Du;Jinglai Shen
中科院分区：
文献类型：
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作者：
Hanyu Zhang;Lili Du;Jinglai Shen
本研究旨在发展一种以车队为基础的合作式换道控制系统。该方法在以队列为中心的队列控制下协调CAV队列的轨迹，以适应CAV从相邻车道的变道请求，在保证CAV安全性和机动性的前提下，减少变道机动对队列交通的负面影响。在数学上，PB-CLC控制建立使用混合模型预测控制（MPC）系统。混合MPC系统包括一个基于MPC的混合整数非线性规划优化器（MINLP-MPC）的最佳换道决策，它考虑了多个目标，如交通平稳性，驾驶舒适性和换道响应灵敏度受到车辆动力学和安全约束。为了保证换道的可行性，通过分析MINLP-MPC模型的可行性，研究并给出了换道时间窗的下限。除了最佳的换道决策考虑，混合MPC系统的设计，以确保控制的连续性和平滑性。特别是，混合MPC系统控制的可行性和稳定性证明，使车队的来回状态之间的车辆跟驰和换道适应状态切换。接下来，我们开发了一种机器学习辅助的分布式分支定界算法（ML-DBB）来在控制采样时间间隔（< 1秒）内求解MINLP-MPC模型。具体而言，建立在计算机模拟和c-LHS采样技术，监督机器学习模型离线开发预测的整数变量，这是进一步集成到分布式分支和定界方法，以解决MINLP-MPC模型有效地在线减少的解决方案空间。大量的数值实验验证了ML-DBB算法和PB-CLC控制的有效性和适用性。
This study is devoted to developing a platoon-based cooperative lane-change control (PB-CLC). It coordinates the trajectories of a CAV platoon under a platoon-centered platooning control to accommodate the CAV lane-change requests from its adjacent lane, aiming to reduce the negative traffic impacts on the platoon resulting from lane-change maneuvers, on the premise of ensuring CAVs’ safety and mobility. Mathematically, the PB-CLC control is established using a hybrid model predictive control (MPC) system. The hybrid MPC system involves an MPC-based mixed integer nonlinear programming optimizer (MINLP-MPC) for optimal lane-change decisions, which considers multiple objectives such as traffic smoothness, driving comfort and lane-change response promptness subject to vehicle dynamics and safety constraints. To ensure the feasible lane-change, this study investigates and provides a lower bound of the lane-change time window by analyzing the MINLP-MPC model feasibility. Apart from the optimal lane-change decision consideration, the hybrid MPC system is well designed to ensure the control continuity and smoothness. In particular, the hybrid MPC system control feasibility and stability are proved to enable the platoon's back-and-forth state switchings between car-following and lane-change accommodation states. Next, we developed a machine learning aided distributed branch and bound algorithm (ML-DBB) to solve the MINLP-MPC model within a control sampling time interval (< 1 second). Specifically, built upon computer simulation and the c-LHS sampling technique, supervised machine learning models are developed offline to predict a reduced solution space of the integer variables, which is further integrated into the distributed branch and bound method to solve the MINLP-MPC model efficiently online. Extensive numerical experiments validate the effectiveness and applicability of the ML-DBB algorithm and the PB-CLC control.