## 1 Introduction  
The high collision risk and frequent congestion that arise from traffic merge regions in highway networks are closely related to the frequent and required merging behaviors, especially the transition procedures from an on-ramp to the main roadway [1; 2]. Among existing research, gap acceptance theory has been extensively explored, which provided a simplified framework for understanding merging behavior mechanism based on gap size and surrounding vehicle positions [3; 4; 5]. Some scholars have also delved into the impact of external variables on merging behavior at a medium granularity [6], and have thereby further strengthened the understanding of merging behavior mechanism.  
Even with the advancements in integrating behavior analysis, there still remain gaps that need more research.  
Data Processing and Trajectory Extraction  
The existing dataset processing phase is often treated as a black box. The obtained results are difficult to replicate, and the accuracy of conclusions are challenging to verify. The great precision and richness of HD maps, along with their advancement, present an opportunity to improve the accuracy of data processing. However, there is currently no research on how to effectively utilize the vast amount of data in HD maps to study driving behavior.  
Classification of Merging Patterns  
Existing studies categorized merging types into free merging, cooperative merging, and forced merging, with a focus on the impact of merging vehicles on lead and lag vehicles in the target lane [7]. [PERSON] et al. categorized merging patterns into nine modes based on the merging speed and acceleration [6]. [PERSON] categorized merging patterns into four types--no cars, cars in front, cars behind, and cars both in front and behind [8]. Many scholars investigated merging behavior patterns, often focusing on specific macro traffic flow conditions or considering variations in a single variable over time while neglecting spatial dynamics. In general, their categorization lacks generalizability.  
In-depth Analysis of Driving Behavior  
Existing studies often fall short in providing a comprehensive perspective. Some of them failed to consider the temporal aspect adequately, focusing solely on specific time frames [9]. Others overlooked the importance of spatial dimension sub-division within scenarios, which is critical in understanding driving behavior intricacies. Furthermore, existing research often ignored the inherent heterogeneity among different vehicle types.  
To bridge the above gaps, we aim to unravel spatial-temporal patterns and the heterogeneity of merging behavior. Specifically, we initially established a trajectory extraction framework utilizing HD maps, aimed at precisely capturing semantic relationships among traffic participants, thereby ensuring accurate data for trajectory mining. Subsequently, we integrate both temporal and spatial dimensions to develop a method for the classification of merging behavior patterns. Finally, we conduct similarity tests to analyze the characteristics of merging patterns, identifying high-risk scenarios in the process.  
The rest of the paper is organized as follows: Section 2 provides a literature review. Section 3 details the processing of the dataset and the establishment of a trajectory extraction framework based on HD maps. Section 4 presents the results and discussion, including the spatiotemporal merging pattern classification and heterogeneity analysis. Conclusions and recommendations for future research end the paper.