## 4 Results and Discussions
### Discussion of the Results  
This study introduces a framework for extracting merging trajectories using HD maps, providing valuable insights into driving behavior analysis with map references. A spatiotemporal analysis of interactions across various merging patterns can be a vehicle through the subdivision of these patterns. The following new findings are discovered: (1) Car and van drivers exhibit a conservative merging preference, often merging with a vehicle in front but none behind. Specifically, truck drivers tend to leave larger gaps in front for safety, a practice vital for refining autonomous vehicle algorithms. (2) The merging patterns B and E, or C and F, exhibit no discernible difference in terms of merging distance, speed, or duration. As a result, these matching patterns can be combined during traffic simulation. (3) The design of automated driving planning and control must consider high-risk scenarios, such as merging patterns C and F. Additionally, careful consideration is required for vehicle merging in truck platooning situations.  
This research holds significant promise for real-world applications, particularly in enhancing road safety and the efficiency of autonomous driving systems:  
(1) Traffic Management and Safety  
Our findings contribute to improving highway safety and efficiency by offering a nuanced understanding of merging behaviors. By leveraging this research, traffic planners and policymakers can make informed decisions regarding the design and management of merge zones. This includes optimizing the placement of dashed versus solid lane markings and adjusting speed limits to mitigate traffic congestion and reduce the incidence of accidents.  
(2) Autonomous Vehicle Technology  
The insights garnered from this study are instrumental in refining the decision-making algorithms of autonomous vehicles. By integrating our findings into these systems, autonomous vehicles can achieve a more sophisticated simulation of human driving behaviors. This enhancement will lead to improvements in the safety, comfort, and overall adaptability of autonomous driving technologies, ensuring a smoother integration into existing traffic systems.  
In essence, the theoretical contributions of our research are paralleled by its practical value, offering actionable insights for both policymakers and researchers in the field of automated driving technology. These implications extend beyond academic discourse, promising to inform and shape the future landscape of traffic management and autonomous vehicle development. Due to limitations in the dataset, there is insufficient data to analyze van behavior comprehensively. Future studies could focus on examining continuous merging behavior at location0 for a more detailed understanding.  
Considering that large-scale aerial vehicle traffic trajectory datasets involving individual driver characteristics may touch upon privacy issues, and existing datasets that include drivers' psychological and physical traits are unable to reach the scale of the exiD dataset, future research needs to find a better balance between large-scale vehicle trajectory data and drivers' mental and physical health, legal regulations, road types, and human driving behaviors. Although this study has some shortcomings in certain characteristics, by introducing HD maps for the first time, it lays the foundation for more comprehensively integrating these features in future research.