Determining the steering direction in critical situations: A decision tree–based method. (17th August 2020)

• Record Type: Journal Article
• Authors: Zhou, Huajian; Zhong, Zhihua; Hu, Manjiang; Huang, Jin
• Is Part Of: Traffic injury prevention. Volume 21:Number 6(2020)
• Page Start: 395
• Page End: 400
• Publication Date: 2020-08-17
• Subjects: Decision of steering direction -- evasive maneuvers -- decision tree classifier -- critical situations -- NASS-CDS data set; Traffic safety -- Periodicals; Traffic accidents -- Periodicals; Wounds and injuries -- Prevention -- Periodicals; 363.125
• DOI: 10.1080/15389588.2020.1771326
• Languages: English
• ISSNs: 1538-9588

Abstract

Abstract: Objective: Factors related to the driver–vehicle–environment system have a significant influence on a driver's decision to perform evasive maneuvers, especially the decision of steering direction (DSD) in critical situations. However, few studies have systematically investigated the relationships between these factors and DSD. The objective of this study is to analyze and model drivers' DSD in critical situations. Methods: Data from the NASS-CDS from 1995 to 2015 were utilized in this study. The decision tree (DT) classifier was utilized to model a driver's DSD for both intersection-related and non-intersection-related subsets, combined with a 10-fold cross-validation technique and grid search approach to evaluate and optimize the model. An analysis of variable importance was also conducted. Results: Two separate DT models of drivers' DSD were obtained based on the optimized hyperparameters, with test accuracies of 84.6% (intersection-related) and 79.2% (non-intersection-related). The variable DIFFANGLE (angle difference between 2 vehicles) ranked as the most important factor influencing drivers' DSD in both models. The variables, in order of importance, were SPEED (travel speed of the subject vehicle) and AGE (driver's age) for the intersection-related model and SPEED, PREMOVE (pre-event movement), TRAFFLOW (trafficway flow), and AGE for the non-intersection-related model. Moreover, an interesting same direction pattern was observed in both DT models. Conclusions: This study employed NASS-CDS data and DT classifiers to analyze and model drivers' DSD behavior. The test accuracies for both classifiers were acceptable. Potential variables influencing drivers' DSD were explored, which improves the research on evasive behavior in lateral movement and promotes further applications for intelligent vehicles using the constructed models.