水流能够在复杂自然环境中自主探索出合理的流向和可行的路径，借鉴该特性提出一种基于虚拟流场的自动驾驶全局路径规划方法。首先，基于流体动力学理论建立虚拟道路流场的数学模型，并采用计算流体力学方法对流场状态进行数值求解，从而在大尺度的复杂道路路网中生成多条从起点到终点的全局流线；同时，通过构建规划路径的综合评价函数，从多条流线中筛选出最优的全局路径；最后，设计GNSS/INS组合定位系统采集真实路网数据并构建全局静态道路地图，在校园场景下对提出的方法进行试验验证。试验结果表明，基于虚拟流场的全局路径规划方法，能够根据自动驾驶任务规划并筛选出完整、平滑的全局最优路径，并且避免出现了陷入环境局部极小点的无解情况。

According to the characteristic that fluid can naturally explore reasonable flow direction and feasible path in complex environment, a global path planning method for autonomous driving based on virtual flow field is proposed. Firstly, a mathematical model of virtual road flow field is established based on the theory of fluid dynamics, and the computational fluid dynamics method is adopted to numerically solve the state of flow field, so as to generate multiple streamlines from the starting point to the destination in a large-scale complex road network. Meanwhile, by constructing a comprehensive evaluation function of the planned path, the optimal global path is selected from multiple streamlines. Finally, a combined GNSS/INS positioning system is designed to collect real data of road network and build a global static road map, and the proposed method is experimentally verified in a campus scenario. The experimental results show that the global path planning method based on virtual flow field can plan and select a complete and smooth global optimal path according to the automatic driving task and avoid the non-solution situation that falls into the local minimum of the environment.

重庆市科学技术局农业农村领域重点研发项目(cstc2021jscx-gksbX0003);重庆市研究生科研创新项目(CYS23207)