  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.
