Traffic Flow-Based Crowdsourced Mapping in Complex Urban Scenario

An accurate road topological structure is of great importance for autonomous driving in complex urban environments. Currently, most autonomous vehicles highly rely on the High-Definition map (HD map) to cruise across the city. Without the prior map, it's hard for vehicles to find right-turning and left-turning ways in large intersections. However, due to the complexity of intersections, producing such a map by human resources is time-consuming and error-prone. In this paper, we proposed a framework to automatically produce the topological map of complicated intersections. This framework adopts the crowdsourcing way to collect semantic information about the environment and traffic flows. The topological structure is inferred from traffic flows correctly and automatically. We highlight that this framework is highly automatic and scalable, which can greatly speed up HD map production and decrease the cost. The proposed system is validated by real-world crowdsourcing data and the result is comparable to the traditional HD maps.