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A Validation Study on a Subjective Driving Workload Prediction Tool
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- Authors
- Yoonsook Hwang, Daesub Yoon, Hyun Suk Kim, Kyong-Ho Kim
- Issue Date
- 2014-08
- Citation
- IEEE Transactions on Intelligent Transportation Systems, v.15, no.4, pp.1835-1843
- ISSN
- 1524-9050
- Publisher
- IEEE
- Language
- English
- Type
- Journal Article
- Abstract
- A variety of methods used to measure a driver's workload do not include information such as the driver's characteristics and attitudes. A subjective driving workload prediction tool (DWPT) was developed to overcome this limitation. The purpose of this study is to validate the DWPT, which is composed of three subfactors: the situational inadaptability, the risk-taking personality, and the interpersonal inadaptability. For this reason, we conducted the driving simulator experiment to gather the drivers' driving behaviors. The driving path scenario included various driving tasks. Thirty male drivers participated in this study. The analysis results showed that a driver's predicted score of subjective driving workload had a positive or a negative relation to their workload-related driving behaviors such as the operation of the indicator/steering/gas pedal and gaze behaviors. In particular, two subfactors, i.e., the risk-taking personality and the interpersonal inadaptability, were more closely related to their driving behaviors than the total predicted subjective driving workload and the situational inadaptability subfactor. These results suggest that a DWPT could be used to predict the drivers' subjective driving workload instead of measuring the driving performance or self-reporting questionnaire. In addition, this would be expected to be available on the area of the Advanced Driver Assistance System and drivers' safety industry. © 2014 IEEE.
- KSP Keywords
- Advanced driver assistance systems(ADAS), Driving Performance, Driving simulator experiment, Prediction tool, S characteristics, Validation study, Workload prediction, driving behavior, risk-taking, self-reporting