Autonomous driving technology and platooning driving technology are important directions for the development of intelligent and connected vehicles. Aiming at the motion control problem of autonomous vehicle platoon, this paper proposes a multilayer predictive control framework (MPCF) based on heuristic learning agent and improved distributed model. Firstly, the leading autonomous vehicle and following heterogeneous vehicles are modeled respectively, and the motion control problem of autonomous platoon is described. Then, the multilayer motion control framework is designed, which contains highly automated tracking control optimization for the leading vehicle (LV) and high-precision formation keeping optimization for the following vehicles (FVs). In the upper layer, the heuristic Dyna algorithm based predictive control (HDY-PC) method is proposed to improve the path tracking performance of the leading vehicle. In the lower layer, the improved distributed model based predictive control (IDM-PC) method is developed to guarantee the motion effectiveness and stability of the vehicle platoon. Besides, the multilayer control framework can handle various communication topologies and dynamic cut-in/cut-out maneuvers. The virtual environment simulation shows that the proposed motion control framework for heterogeneous autonomous vehicle platoon achieves better performance in path tracking and platoon keeping. The adaptability of the framework is also verified using another real-world scene.

中文翻译：

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使用多层预测控制框架的异构自主车辆队列的高效运动控制


自动驾驶技术和编队驾驶技术是智能网联汽车发展的重要方向。针对自动驾驶车队的运动控制问题，提出一种基于启发式学习代理和改进的分布式模型的多层预测控制框架（MPCF）。首先，分别对领先的自主车辆和跟随的异构车辆进行建模，并描述了自主队列的运动控制问题。然后，设计了多层运动控制框架，其中包含对前导车辆（LV）的高度自动化跟踪控制优化和对跟随车辆（FV）的高精度编队保持优化。在上层，提出了基于启发式Dyna算法的预测控制（HDY-PC）方法来提高前导车辆的路径跟踪性能。在下层，开发了改进的基于分布式模型的预测控制（IDM-PC）方法来保证车辆队列的运动有效性和稳定性。此外，多层控制框架可以处理各种通信拓扑和动态切入/切出操作。虚拟环境仿真表明，所提出的异构自动驾驶车辆队列运动控制框架在路径跟踪和队列保持方面取得了更好的性能。该框架的适应性也通过另一个真实场景得到验证。