Investigating Lane Departure Warning Utility with Survival Analysis Considering Driver Characteristics

Previous studies have focused on the impact of individual factors on lane departure warning (LDW) utility during driving. However, comprehensive analysis has not been considered based on multiple variables, such as driver characteristics. This paper aims to propose a methodology in exploring the utility of LDW under varied warning timing situations, focusing on changes in driving style and distraction level to obtain the optimal warning timing matching relationship. A driving simulator experiment with a mixed 4 × 3 factor design was conducted. The design matrix includes four level of secondary task (ST) conditions and three warning timings situations for drivers with various driving styles. To estimate the utility of the LDW system, lane departure duration (LDD) was selected as a time-based measure of utility. Both the Kaplan-Meier method and COX model were applied and compared. Combined with questionnaire results, the results indicate that both driving style and distraction state are significant influence factors. Generally, the results suggest that the more aggressive drivers lead to the more severe lane departure behavior and they preferred late warning. In terms of distraction state, the LDD increases with the level of ST remarkably. This implies that the earlier warning timing should be given for the higher-level distraction state condition. It was also observed that adaptive warning timing is needed based on the analysis of the interactive effect among multiple variables. The results provide empirical data for the optimization of LDW system design.

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Publication Details

Journal
Applied Sciences
Published
2024-10-12
DOI
https://doi.org/10.3390/app14209317
Citations
1
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
0.64
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article

Investigating Lane Departure Warning Utility with Survival Analysis Considering Driver Characteristics

Tong Zhang, Zixi Wang, Mingfang Zhang, Xiaofan Zhao
1 citations
Applied Sciences
Human-Automation Interaction and Safety
0.64
article

Investigating Lane Departure Warning Utility with Survival Analysis Considering Driver Characteristics

Tong Zhang, Zixi Wang, Mingfang Zhang, Xiaofan Zhao
article en
1 citations

Abstract

Previous studies have focused on the impact of individual factors on lane departure warning (LDW) utility during driving. However, comprehensive analysis has not been considered based on multiple variables, such as driver characteristics. This paper aims to propose a methodology in exploring the utility of LDW under varied warning timing situations, focusing on changes in driving style and distraction level to obtain the optimal warning timing matching relationship. A driving simulator experiment with a mixed 4 × 3 factor design was conducted. The design matrix includes four level of secondary task (ST) conditions and three warning timings situations for drivers with various driving styles. To estimate the utility of the LDW system, lane departure duration (LDD) was selected as a time-based measure of utility. Both the Kaplan-Meier method and COX model were applied and compared. Combined with questionnaire results, the results indicate that both driving style and distraction state are significant influence factors. Generally, the results suggest that the more aggressive drivers lead to the more severe lane departure behavior and they preferred late warning. In terms of distraction state, the LDD increases with the level of ST remarkably. This implies that the earlier warning timing should be given for the higher-level distraction state condition. It was also observed that adaptive warning timing is needed based on the analysis of the interactive effect among multiple variables. The results provide empirical data for the optimization of LDW system design.

Applied SciencesVol. 14(20)
North China University of Technology (CN)
Openalex Percentile: Top 26%
Human-Automation Interaction and Safety
0.64
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Investigating Lane Departure Warning Utility with Survival Analysis Considering Driver Characteristics — Tong Zhang, Zixi Wang, et al. · Applied Sciences (2024) | TGRS Research Map | TGRS