There is extensive literature on perceiving road structures by fusing various sensor inputs such as lidar point clouds and camera images using deep neural nets. Leveraging the latest advance of neural architects (such as transformers) and bird-eye-view (BEV) representation, the road cognition accuracy keeps improving. However, how to cognize the ``road'' for automated vehicles where there is no well-defined ``roads'' remains an open problem. For example, how to find paths inside intersections without HD maps is hard since there is neither an explicit definition for ``roads'' nor explicit features such as lane markings. The idea of this paper comes from a proverb: it becomes a way when people walk on it. Although there are no ``roads'' from sensor readings, there are ``roads'' from tracks of other vehicles. In this paper, we propose FlowMap, a path generation framework for automated vehicles based on traffic flows. FlowMap is built by extending our previous work RoadMap, a light-weight semantic map, with an additional traffic flow layer. A path generation algorithm on traffic flow fields (TFFs) is proposed to generate human-like paths. The proposed framework is validated using real-world driving data and is amenable to generating paths for super complicated intersections without using HD maps.

翻译：关于通过融合激光雷达点云与摄像头图像等传感器输入来感知道路结构的研究已有大量文献，深度神经网络的应用尤为广泛。借助Transformer等神经架构与鸟瞰图（BEV）表示的最新进展，道路认知精度持续提升。然而，对于不存在明确定义"道路"的自动驾驶场景（例如在没有高清地图的情况下，如何在交叉路口内部找到路径），因其既缺乏"道路"的显式定义，又缺少车道标线等显式特征，始终是一个悬而未决的难题。本文灵感源自一句谚语："世上本无路，人走多了便成了路。"尽管传感器读数中不存在"道路"，但其他车辆的行驶轨迹却隐含了"道路"的语义。为此，我们提出FlowMap——一种基于交通流的自动驾驶车辆路径生成框架。FlowMap通过在我们前期工作RoadMap（一种轻量级语义地图）中新增交通流层构建而成。针对交通流场（TFF）提出路径生成算法，可生成类人路径。基于真实驾驶数据的验证表明，该框架无需高清地图即可为超复杂交叉路口生成可行路径。