## 7 Conclusions  
This research investigated the driving styles of heavy-duty truck drivers when passing through different types of intersections. At the same time, based on the data, a method of classifying driving styles at intersections is proposed, and a probability model of drivers' driving-style selections at different types of intersections is obtained. First, a field driving experiment was conducted to obtain the kinematic data of heavy-duty truck drivers at intersections, including longitudinal acceleration, lateral acceleration, and speed data. In addition, GPS data was recorded. Then, all data were processed into four derivative features: mean acceleration (\(|\overline{a}|_{mean}\)), standard deviation of acceleration (\(|\overline{a}|_{std}\)), mean jerk (\(|\overline{f}|_{mean}\)), and standard deviation of jerk (\(|J|_{std}\)). The K-means cluster method was applied to classify the three driving styles at different intersections. Finally, an MNL model was used to analyze the probability of drivers' driving-style choices at different types of intersections.  
Previous research has focused on passenger drivers and driving styles on roads. Most research on intersections has focused on accident data. In this paper, we propose a new framework to investigate the driving safety of heavy-duty truck drivers at intersections. The framework can identify drivers' driving styles at intersections and calculate the proportion of drivers' driving styles at intersections on a trip. The results of this study will provide safety guidance and warning advice for heavy-duty drivers before they pass through intersections, based on the statistics of driving styles. At the same time, based on thresholds of the different driving styles of truck drivers, the influences of different types of intersections on driving styles have been analyzed. The results help provide safety reminders for different types of drivers at specific intersections to improve driver safety and the overall safety of the intersection. The research makes a certain contribution to route planning and preparation, the development of advanced driver assistance systems, assessments of drivers' levels, and the avoidance of dangerous driving at intersections. Overall, the research results will contribute to active driving safety protection.  
Future research should adopt a larger sample size and explore the effects of personal attributes and more types of intersections on groups of drivers, while taking into consideration the effects of the driver's age, gender, and education level on driving choices and behaviors. After analyzing the impacts of the four types of intersections on driving styles, further study could extend to the intersection types' comprehensive influence on drivers' driving styles. Future research will also consider the vehicle's load state, driver's fatigue level, sleep quality, and other factors affecting a driver's driving style at different types of intersections.  
The authors confirm contribution to the paper as follows: study conception and design: Y.M. and S.C.; data collection: Y.Z. and Q.P.; analysis and interpretation of results: Y.M. and Y.Z.; draft manuscript preparation: A.J.K. and Y.Z. We certify that the manuscript has not been submitted to other journals. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the National Natural Science Foundation of China (Grant No. 52172342).  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data supplied in this research can be attained on request from the corresponding author.  
The authors declare no conflict of interest.