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.

Accurate and robust localization remains a significant challenge for autonomous vehicles. The cost of sensors and limitations in local computational efficiency make it difficult to scale to large commercial applications. Traditional vision-based approaches focus on texture features that are susceptible to changes in lighting, season, perspective, and appearance. Additionally, the large storage size of maps with descriptors and complex optimization processes hinder system performance. To balance efficiency and accuracy, we propose a novel lightweight visual semantic localization algorithm that employs stable semantic features instead of low-level texture features. First, semantic maps are constructed offline by detecting semantic objects, such as ground markers, lane lines, and poles, using cameras or LiDAR sensors. Then, online visual localization is performed through data association of semantic features and map objects. We evaluated our proposed localization framework in the publicly available KAIST Urban dataset and in scenarios recorded by ourselves. The experimental results demonstrate that our method is a reliable and practical localization solution in various autonomous driving localization tasks.