Nihon Kikai Gakkai ronbunshu (Dec 2022)
Component analysis of driver’s subjective risk level at pedestrian appearance and examination by an actual vehicle test
Abstract
This paper discusses explanatory variables that constitute the driver’s subjective risk level when encountering a pedestrian who suddenly pops out from the roadside. In this research, near-miss incident data, called “Hiyari-hatto data,” against pedestrian recorded by drive recorders equipped on taxis are used. First, we evaluate the subjective risk level of 90 near-miss incidents quantitatively in 6 levels and select the candidate of eight explanatory variables. Second, Principal Component Analysis (PCA) is applied to determine the principal components of the subjective risk level. Moreover, Multiple Regression Analysis (MRA) is used to compare the contribution of explanatory variables. From these analyses, some explanatory variables have become effective candidates. Time to collision (TTC) and its related variables might be the main explanatory variable and collision timing might be another explanatory variable. However, it is difficult to determine that collision timing is a sufficiently influential variable by PCA and MRA. Therefore, the latter of this paper examines the possibilities of collision timing (in our experiment, qualitative collision timing was evaluated using quantitative value as “collision gap” which is the difference between TTC and TTV) as a candidate explanatory variable by an actual vehicle test. To reveal the relation between subjective risk level and explanatory variables, in particular the collision gap, the driver’s several reactions are collected by an actual vehicle test in which a pedestrian suddenly pops out from the roadside to in front of the vehicle. Based on the about 900 case experimental data, we examined the possibilities of “collision gap” as an explanatory variable in subjective near-miss incident level.
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