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The Study on the Prediction of Driving-workload using the DWPT in Curve Section: Local Road and Urban Road
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- Authors
- Yoonsook Hwang, Daesub Yoon, Hyunsuk Kim, Kyong-Ho Kim
- Issue Date
- 2014-05
- Citation
- Asian Conference on Ergonomics and Design (ACED) 2014, pp.445-448
- Language
- English
- Type
- Conference Paper
- Abstract
- This study aimed to investigate on whether the DWPT (the subjective Driving-Workload Prediction Tool) could be identified driving-workload according to road characteristics: the local road and the urban road. We had performed statistical analysis using the data of 26 drivers (male: 15, female: 11; age: 36.54(SD=14.28)) from real driving environment. The DWPT score and EEG data were analyzed. The participants asked to fill out the DWPT Questionnaire before starting driving experiment. EEG data were collected using the FOT (Field Operational Test) method during main driving experiment. The DWPT is the developed questionnaire for predicting on drivers' subjective driving-workload based on drivers' attitude on driving and their psychological characteristics in previous study. The DWPT is composed of three sub factors: the Situational Inadaptability, the Interpersonal Inadaptability, and the Risk Taking Personality. In this study, we had performed the regression analysis by setting the DWPT as an independent variable. As a result of analysis, the total score of DWPT had predicted driving-workload significantly while driving in the curve at both local and urban roads. However, the sub-factors of DWPT, the Situational Inadaptability, the Interpersonal Inadaptability, and the Risk Taking Personality, had predicted driving-workload inconsistently according to different road types. For details, the situational inadaptability was predicted driving-workload significantly during driving on the curve of both types of road. However, the interpersonal inadaptability was tended to predict driving-workload slightly on the curve in only urban road. These results implicate that the density of driving environments (e.g. number of pedestrians and number of other vehicles) may affect driving-workload while curve negotiation. In other words, there are more pedestrians and more vehicles during curve negotiation in urban road than in local road. Therefore, the drivers should be driving more carefully on curve in urban road while interacting with others. These results suggested that the DWPT possibly identify differences of driving environments. The DWPT and the results of study will be applied to the driving-workload management system and adaptive driver intelligent human-vehicle interaction system. These systems could estimate the drivers' driving-workload and provide intelligent interaction system for drivers by multi-modal interfaces based on the driving-workload.
- KSP Keywords
- Curve negotiation, EEG data, Field operational test, Human-vehicle interaction, Intelligent interaction, Interaction System, Management system, Multimodal interface, Prediction tool, Regression analysis, Risk taking