## 5 Conclusions  
This study investigated the impact of driving style and distraction state on the utility of various lane departure warning modes. A study involving 91 participants was conducted using the driving simulator and 80 participants were selected based on driving style classification algorithm. Survival models including the K-M method and COX model were adopted to explore the relationship between driver characteristics and warning effectiveness. LDD, the lane departure duration, was used as the survival time to describe the risk of departure in lane-keeping conditions. The utility of lane departure warning modes was compared using the K-M method through situations with different driving styles and ST conditions, respectively. Then, the COX model was used to quantify the impact of these variables on the utility of LDW modes under various combination conditions, setting the situation with no warning as the baseline. Moreover, the five-level Likert scale was used to investigate the subjective acceptance of LDW systems for the participants with different styles. The results show that undistracted participants (no ST condition) are suitable for late warning. When the distracted state is considered, early warning applies to cautious drivers. Late warning is suitable for moderate and aggressive drivers in low-level ST condition, and early warning is suitable for these drivers in high-level ST condition. It is noteworthy that in medium-level ST condition, early warning is suitable for moderate drivers while the late warning is applicable to aggressive drivers. This study provides evidence that advanced LDW systems should consider the driving style and distraction state. In future research, further experimentation and analysis are needed to determine whether the proposed LDW strategy is applicable to drivers in other countries and regions. We also need to explore driver acceptance of LDW systems based on the analysis of more factors, including driving mood, experience, road curvature, and the interaction among vehicles with LDW systems to improve road safety.  
Conceptualization, M.Z. and Z.W.; methodology, M.Z. and X.Z.; software, M.Z.; validation, M.Z.; formal analysis, M.Z. and Z.W.; investigation, M.Z., Z.W. and T.Z.; resources, M.Z. and Z.W.; data curation, M.Z. and X.Z.; writing--original draft preparation, M.Z.; writing--review and editing, M.Z.; visualization, M.Z.; supervision, M.Z. and X.Z.; project administration, M.Z. and T.Z.; funding acquisition, M.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by R&D Program of Beijing Municipal Education Commission, grant number KM202210009013.  
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of North China University of Technology (protocol code 2024011521).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study to publish this paper.  
The data presented in this study are not publicly available due to privacy and ethical restrictions. The dataset contains sensitive driving behavior information related to lane departure warning, and sharing this dataset publicly could compromise privacy. The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.