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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.
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Ji Hoon Kim
Ji Hoon Kim received his Bachelor’s degree from the department of Industrial Engineering at Hongik University in 2014. He is currently in the department of Industrial Engineering at Hongik University as a graduate student pursuing his Master’s degree.
Ji Hyoun Lim
Ji Hyoun Lim received BSE, Industrial Engineering from Seoul National University in 2000 and Ph.D. Industrial and Operations Engineering from University of Michigan, Ann Arbor in 2007. She worked in Design Team at Mobile division of Samsung Electronics from 2007 to 2010. She is currently in School of Design and Human Engineering, UNIST, Korea as an assistant professor.
Chun Ik Jo
Chun Ik Jo received his Bachelor’s and Master’s degree in Industrial Engineering from Hongik University in 2012 and 2014 respectively. He is currently a researcher at Advanced Technology Exhibition Divison of Gwacheon National Science Museum, Minister of Science, ICT and Future Planning, Korea.
Kyungdoh Kim
Kyungdoh Kim received Bachelor’s and Master’s degree in the Department of Industrial Engineering at Seoul National University in 1999 and 2003 respectively. He received Ph.D. Degree in the School of Industrial Engineering at Purdue University in 2007, and specialized in the Human Factors area, Human-Computer Interaction. He has been an assistant Professor in the Department of Industrial Engineering at Hongik University since 2012.