Detection of Dangerous Drivers based on their Weak Cognitive Ability using the Empathizing-systemizing Model and the Real-time UFOV Method

Abstract

Introduction: Many traffic accidents are caused by driver-related errors, with cognitive ability being the most important direct cause. Since not all drivers are dangerous (accident-prone), the most effective method to prevent accidents is to detect a “dangerous” driver, which is, driver in weak cognitive ability before a traffic accident occurs and to what extent weak in his cognitive ability during driving. This study is intended to detect drivers’ inherent cognitive styles as the primary cause of traffic accidents.

Method: This study utilized a web questionnaire survey [1] to collect examples of both traffic accidents and near misses that occured during ordinary driving, classifying participants into three cognitive style of E-S types (Type E, B, and S) with Empathizing-Systemizing (E-S) model and its Empathy Quotient (EQ) and Systemizing Quotient (SQ) scales as indices of cognitive traits to detect the relationship between traffic accidents/near misses experienced in the past.

Results: The outcomes allowed us to discriminate between a "safe" driver, Type E, and a "dangerous" driver, Type S, who scored highest for the number of near misses. We could identify those differences and measure in terms of driver’s visual attention capability (gaze response speed) under the driving simulation environment incorporating with the rUFOV method [2].

Conclusion: The potential of weak cognition style can be detected in advance of driving with E-S type, and to measure “degree of accident-proneness” in advance of driving, since not all drivers are equally “dangerous”. Then, we propose that the E-S model can be incorporated into the driver aptitude test (before a licence provision).