Autonomous vehicles have been used for a variety of driving tasks, in which path planning and motion control are important research parts to realize the autonomous driving. A hierarchical framework consisting of path planning and motion control of the vehicle for non-specific scenarios is proposed in this paper. Firstly, the description and the formulations of the problem are given, and the corresponding models are constructed. Then, the logical construction of proposed framework is expounded with several logical associations and algorithmic improvements. The bidirectional heuristic planning with adaptive scale search is designed and incorporated with robust weighted regression algorithm to plan the optimal global path, while the multi-step predictive control method based on heuristic reinforcement learning algorithm is proposed to improve the effect of the motion control. The results show that the proposed framework for autonomous driving achieves better performance in both path planning and motion control than several existing algorithms and methods. The adaptability of hierarchical framework is demonstrated. Furthermore, the effectiveness of the hierarchical framework in real world scenario application is also validated.

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 article proposes a multilayer predictive control framework (MPCF) based on heuristic learning agent and improved distributed model. First, 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 LV. 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.

The autonomous vehicle is widely applied in various ground operations, in which motion planning and tracking control are becoming the key technologies to achieve autonomous driving. In order to further improve the performance of motion planning and tracking control, an efficient hierarchical framework containing motion planning and tracking control for the autonomous vehicles is constructed in this paper. Firstly, the problems of planning and control are modeled and formulated for the autonomous vehicle. Then, the logical structure of the hierarchical framework is described in detail, which contains several algorithmic improvements and logical associations. The global heuristic planning based artificial potential field method is developed to generate the real-time optimal motion sequence, and the prioritized Q-learning based forward predictive control method is proposed to further optimize the effectiveness of tracking control. The hierarchical framework is evaluated and validated by the numerical simulation, virtual driving environment simulation and real-world scenario. The results show that both the motion planning layer and the tracking control layer of the hierarchical framework perform better than other previous methods. Finally, the adaptability of the proposed framework is verified by applying another driving scenario. Furthermore, the hierarchical framework also has the ability for the real-time application.