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Conference Paper
A Study on the Cognitive Workload Characteristics according to the Driving Behavior in the Urban Road
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- Authors
- HyunSuk Kim, Daesub Yoon, Seung-Jun Lee, Woojin Kim, Cheong Hee Park
- Issue Date
- 2018-01
- Citation
- International Conference on Electronics, Information and Communication (ICEIC) 2018, pp.826-829
- Language
- English
- Type
- Conference Paper
- Abstract
- The aim of this study is to investigate the cognitive workload characteristics which can be applied to the human factors that are applied to the switching of operation control in autonomous vehicles. For this purpose, we analyze test driver's EEG and driving data measured while driving on real roads to find out the difference of the cognitive workload state according to driving behaviors in the Urban Road. We performed a paired sample t-Test using the preprocessed data to investigate the difference between normal workload ratio and overload workload ratio according to driving behavior. We also performed k-means clustering to see if drivers could be divided into groups using the overload status ratio according to the driving behavior of the driver. For this, we divided the collected data into simple and complex driving types, to see if there is any difference in the cognitive workload when the driver drives straight ahead or does in combination with other driving behaviors. We found that the overload occurrence rate is significantly different according to the driving behavior. We found that female drivers are more likely to be overloaded than male drivers and middle-Aged drivers are likely to have more overloaded than young drivers when they did in complex driving. These cognitive workload characteristics can be reflected in the function of switching the operation control right in the autonomous driving system.
- KSP Keywords
- Autonomous driving system, Autonomous vehicle, Cognitive workload, Human Factors, Middle-Aged, Operation Control, Test driver, Urban roads, Workload Characteristics, Young drivers, driving behavior