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Lim, Ji Hyoun
Intelligent Interaction Lab
Research Interests
  • Computational Cognitive Model, Semantic Network Analysis, Human Robot Interaction

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Utilization of Visual Information Perception Characteristics to Improve Classification Accuracy of Driver’s Visual Search Intention for Intelligent Vehicle

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Title
Utilization of Visual Information Perception Characteristics to Improve Classification Accuracy of Driver’s Visual Search Intention for Intelligent Vehicle
Author
Kim, Ji HoonLim, Ji HyounJo, Chun IkKim, Kyungdoh
Issue Date
2015-10
Publisher
LAWRENCE ERLBAUM ASSOC INC-TAYLOR & FRANCIS
Citation
INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION, v.31, no.10, pp.717 - 729
Abstract
Understanding and predicting a driver's behaviors in a vehicle is a prospective function embedded in a smart car. Beyond the patterns of observable behaviors, driver's intention could be identified based on goal-driven behaviors. A computational model to classify driver intention in visual search which is finding a target with one's eyes as moving selective attention across a search field, could improve the level of intelligence that a smart car could demonstrate. To develop a computational cognitive that explains the underlying cognitive process and reproduces drivers' behaviors, particular parameters in human cognitive process should be specified. In this study, 2 issues are considered as influential factors on a driver's eye movements: a driver's visual information processing characteristics (VIPCs) and the purpose of visual search. To assess an individual's VIPC, 4 psychological experiments-Donders's reaction time, mental rotation, signal detection, and Stroop experiments-were utilized. Upon applying k-means clustering method, 114 drivers were divided into 9 driver groups. To investigate the influence of task goal on a driver's eye movement, driving simulation was conducted to collect a driver's eye movement data under the given purpose of visual search (perceptual and cognitive tasks). The empirical data showed that there were significant differences in a driver's oculomotor behavior, such as response time, average fixation time, and average glance duration between the driver groups and the purposes of visual search. The effectiveness of using VIPC for grouping drivers was tested with task goal classification model by comparing the models' performance when drivers were grouped by typical demographic data such as gender. Results show that grouping based on VIPC improves accuracy and stability of prediction of the model on a driver's intention underlying visual search behaviors. This study would benefit future studies focusing on personalization and adaptive interfaces in the development of smart car.
URI
https://scholarworks.unist.ac.kr/handle/201301/17686
URL
http://www.tandfonline.com/doi/full/10.1080/10447318.2015.1070561
DOI
10.1080/10447318.2015.1070561
ISSN
1044-7318
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DHE_Journal Papers
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