Abstract
With the aim of improving parameter identification and, eventually, evaluating driver distraction with changes in gaze direction, we applied a genetic algorithm (GA) method to identify parameters for an existing vestibulo-ocular reflex (VOR) model. By changing the initial inputs to the GA and fixing two parameters pertaining to the horizontal direction, we achieved improved parameter identification with a lower mean-square error. The influence of driver distraction on eye movement with changes in gaze direction was evaluated from the difference between the predicted and observed VOR in the vertical axis. When a driver was given an additional mental workload, the mean-square error between the measured and simulated values was bigger than that in the absence of the mental workload. This confirmed the relationship between driver distraction and eye movement in the vertical direction. We hope that this method can be applied in evaluating driver distraction.
Highlights
The influence of driver distraction on eye movement with changes in gaze direction was evaluated from the difference between the predicted and observed vestibulo-ocular reflex (VOR) in the vertical axis
The semicircular canals [which measure the angular velocity of the head (ω)] and the otolith organs [which measure linear accelerations of the head (α) and gravity (g)] sense the proprioceptive information from the environment; this is the first step in calculating sensory information from the measurements
Data from 15 subjects who drive every day were collected for parameter identification
Summary
The vestibular system, which is a sensory mechanism in the inner ear, provides the principal contribution to the sense of balance and spatial orientation. When the head is turned to the left, the eyes move in the opposite direction to stabilize the visual world based on the input to the vestibular organ. We used the VOR model proposed by [1], which reflects interactions between the otoliths and the semicircular canals. In this model, head movement is represented by a linear acceleration and an angular velocity as inputs, and the movement of the eyeball is the output [2] (Figure 1). The researchers found a tendency toward a relationship, but the number of subject was insufficient for the purpose of making a decision Their parameter identification by using a hybrid genetic algorithm left a gap between simulation and measurement. We only considered movements in vertical direction, which have a strong effect on eye movement
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