Variable Selection and Modeling of Drivers' Decision in Overtaking Behavior Based on Logistic Regression Model With Gazing Information

• Published in: IEEE access Vol. 9; pp. 127672 - 127684
• Main Authors: Nwadiuto, Jude C., Yoshino, Soichi, Okuda, Hiroyuki, Suzuki, Tatsuya
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Data models; Decision making; Driver behavior; gazing behavior; Hidden Markov models; Input variables; Intelligent vehicles; line-of-sight information; logistic regression; Logistics; Mathematical model; model selection; Overtaking behavior; Regression models; Statistical analysis; statistical test; Statistical tests; Switches; Variables; Vehicles
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3111753

This paper investigates the decision-making characteristics of the driver in the overtaking on the highway road. For the research purpose, a novel method was proposed by introducing a logistic regression model accompanied by the statistical test technique, which does not require prior knowledge about the explanatory variables. This study hypothesizes that the driver's gazing behavior is crucial for the decision-making process in driving and hence, the line-of-sight information was introduced to estimate driver's gazing behavior in the model of driver's decision specifically for reproducing the overtaking driving behavior accurately. Consequently, the proposed model realized a high describability on the decision of the driver when performing the overtaking driving task, which is one of the significant advancements of the present study with respect to the past similar studies. This study integrates the perspectives of intelligent vehicle design and cognitive science by revealing which factor the driver pays attention to in a changeable driving environment due to various observable factors. In experiments based on the driving simulator with six human subjects, the overtaking behavior was successfully estimated by specifying a set of variables to reconstruct the driver's behavior and then the proposed model provided a minimum set of necessary variables accompanied with key coefficients. In conclusion, the proposed approach based on a simple logistic regression model demonstrated driving behaviors with an accurate estimation of the driver's intention without the need for prior knowledge, and it may contribute to higher describability for various driving actions in a dynamic environment.