## 1 Introduction  
High Definition (HD) routable road map is an indispensable data infrastructure for many Intelligent Transportation System (ITS) applications, such as self-driving, Intelligent Connected Vehicles (ICVs) and Mobility as a Service (MaaS) [1, 2]. As critical hubs of interconnecting roadways, road intersections play a significant role in route guiding [3], traffic controlling [4] and road safety [5]. Generally, road intersections have more complex network structures and more diverse traffic rules than common roadways [6]. Nevertheless, the road intersection information in current map products is limitedly represented on both the level of detail and data freshness, leading to hardly meeting the demands of many ITS applications [7]. Hence, it becomes one of the crucial topics in the field of road map production and data updating to generate the up-to-date and high-detailed models of road intersections [8].  
In the last decades, numerous targeted approaches, such as map vectorization, mobile mapping and remote sensing have been developed for commercial or official road map construction [9, 10, 11]. However, these professional-surveying approaches may be confronted with some vulnerabilities, such as long updating cycle, outdated data source (for map vectorization), expensive data acquisition/processing cost or ticklish object-occlusion problem [9, 12, 13]. Presently, in the new data-collection paradigm of \"citizens as sensors\" [14],some crowdsourced mapping platforms (e.g., OpenStreetMap) make important data supplements for map construction and updating in spite of submissions of varying quality [15, 16]. Particularly, the increasingly amount of crowdsourced trajectory data not only prompt the research on spatiotemporal dynamic analysis [17, 18, 19], but also provide a continuous-dynamic, wide-coverage and low-cost way for constructing routable road map [20, 21, 22]. However, some unpredictable bias and observation errors may occur to crowdsourced trajectories. On the one hand, limited by the equipped GNSS (Global Navigation Satellite System) devices, unstable positioning precision and uneven sampling intervals make crowdsourced trajectories possibly delineate inconsistent shapes of true moving path (see Figure 1a). On the other hand, impacted by uneven road traffic flows, the collected trajectories at different grade roads may show largely varied densities. As shown in Figure 1b, the trajectories collected at high-grade roads are generally highly dense, whereas those trajectories collected at low-grade streets are relatively sparse.  
To address the above-mentioned challenges, the paper proposed a hierarchical segmentation method for generating road intersections from crowdsourced trajectories. The main contributions of the proposed method can be summarized as the following aspects.  
* An adaptive density homogenization operator is performed on raw track data to decrease the uneven density distribution of crowdsourced trajectories.
* A hierarchical segmentation process is implemented to targetedly extract multi-level road intersection elements implied on crowdsourced trajectory data.
* An iterative piecewise fitting method is adopted to approximately delineate the geometric structures of road intersections.  
The remainder of the paper is organized as follows. The state of art road network extraction is systematically reviewed in Section 2. After that, the road intersection extraction method is detailedly elaborated in Section 3. Experimental results and analysis are then presented in Section 4. Finally, Section 5 concludes the paper and outlooks the future works.