A multinomial choice model approach for dynamic driver vision transitions

Huang, Shu-Hsien; Wong, Jennifer T.

doi:10.1016/j.aap.2014.10.010

Cited by 4 publications

(2 citation statements)

References 41 publications

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“…For example, in case of intersection, drivers need to pay more attention on left and right side compared to highway where looking forward is dominant visual behavior. Considering this fact, some studies attempted to classify the eye glances as five (forward, left, right, rear view, in-vehicle) [16], [17]. However, these five categories were not suitable for Markov chain analysis because of the similar situational characteristics with frequent left and right-looking and low frequency of right-looking.…”

Section: Glance Statementioning

confidence: 99%

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Drivers’ Eye Glance Transitions and the Implications on the Microscopic Traffic and Accident Simulation

Kim

Tak

Choi

et al. 2017

WIT Transactions on the Built Environment

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Driver's inattention and distraction are one of the main causes of traffic accidents. Therefore, understanding on the driver's behavior of inattention and distraction gives important implications for improving traffic safety. Previous researches tried to identify the main factors leading to accidents and their impact on traffic accidents. However, it is hard to model and estimate the impact as the drivers' distraction shows stochastic transitions. In this study, we try to provide a better understanding on the relationship between distraction and vehicle safety and analyze the dynamics of driver vision transitions in both normal driving situation and accident situation. Based on the result, we propose a new car-following model, which can reflect the driver's dynamic vision transition, which can regenerate the risk situations that the existing car-following models cannot provide. The new model can provide a framework for better understanding on the relationship of traffic accidents and drivers vision transition.

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Section: Glance Statementioning

confidence: 99%

“…Some studies represented the process of driver's visual attention allocation as a probability of gazing focal point [13]- [15]. In order these limitations, Wong and Huang analyzed the driver's vision transition through attention allocation pattern analysis and multinomial choice model [16], [17].…”

Section: Introductionmentioning

confidence: 99%