一种基于轻量级矢量地图的无人车导航方法

现有的差分全球定位系统通常需要高精度地图数据的支持，然而高精地图制作成本高昂，且 庞大的地图数据对车载电脑性能及网络通讯带宽有较高要求。该文提出一种基于低数据量矢量地图的智能车导航方法，通过引入道路标签机制，从高层次抽象组织道路点，以便快速建立矢量导航地图，并大幅度降低道路点的重复存储。基于该地图的导航方法自动规划最优全局路径，通过基于预瞄点与历史点的几何学算法进行路径跟踪与避障，从而将上位机计算出的方向盘转角、油门和制动踏板深度信息传递到车辆底层控制器，以控制车辆按规划路径行驶。该方法的有效性和准确性在自主开发的无人驾驶平台上得到了验证。

A Low-Cost Vector Map Assisted Navigation Method for Autonomous Vehicle

Traditional differential global positioning system usually demands high-definition map to realize automatic path tracking. However, the high-definition map occupies large storage space and requires highperformance on-board computer or large communication bandwidth. In this paper, a low-cost vector mapbased navigation method for autonomous vehicle is introduced. By recording the vector map offline, the method initializes an optimal global route by giving any starting and ending position on the map. During runtime, the in-vehicle computer filters the real-time positioning data from differential global positioning system and tracks the planned path according to geometric rules. While any obstacles are detected, the system can adjust the local path automatically. In addition, the proposed method can calculate the angle of steering wheel, levels of throttle, brake pedals in real-time, and transmit these actuation commands through controller area network bus interface. Feasibility and accuracy of the proposed navigation method is verified on our autonomous vehicle testing platform.