A Validation Study on a Subjective Driving Workload Prediction Tool

Yoonsook Hwang,Daesub Yoon,Hyun Suk Kim,Kyong-Ho Kim IEEE 2014 IEEE transactions on intelligent transportation sy Vol.15 No.4

<P>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.</P>

The Relations between the Drivers' Subjective Workload Characteristics and Driving Behaviors

Yoonsook Hwang,Daesub Yoon,Kyong-Ho Kim 대한인간공학회 2011 대한인간공학회 학술대회논문집 Vol.2011 No.5

Objective: The aim of this study is to investigate the relations between the drivers' subjective workload characteristics and their driving behaviors. Background: The advances of technologies have led to the development of various systems which provide driving convenience and entertainment to the drivers. These systems provide driving safety and convenience to drivers. However, the usages of these systems possibly increase the traffic accident due to drivers' distraction. Method: Thirty subjects were participated in this experiment. They are all male. They were asked to fill in the DWPT which is composed of 29 questions to predict drivers' subjective driving workload by ETRI and CBNU. They were also asked to fill in questionnaire about drivers' past driving experiences including car accidents before conducting the simulator experiment and then the RSME questionnaire after completing the experiment. Results: The driver group experienced in a traffic accident scored higher "Drivers' Risk-taking Tendency" than non-experienced group. And they had more reported the number of traffic accident as a perpetrator. The relation of drivers' workload(RSME) and their driving behaviors(performance time, indicator operation time etc.) was positive correlation in the U-turn task. Conclusion: As aresult of this study, we need to analyze the cause of the accident for the driver group experienced in a traffic accident whether they are perpetrator or not. In addition, we can predict that drivers' workload may vary depending on the driving tasks. Application: This result will be applied to the development of the system for drivers' workload estimating and management system.

The comparison of the predicted driving workload with the drivers’ physiological information while curve negotiation in the local road

Yoonsook Hwang,Daesub Yoon,Hyunsuk Kim,Changhyun Jeong 대한인간공학회 2013 대한인간공학회 학술대회논문집 Vol.2013 No.5

Objective: The purpose of this study is to investigate the relationship of driving workload between the DWPT(the subjective Driving-Workload Prediction Tool) developed by ETRI and EEG data collected from real driving environments on curve negotiation in the local road. Background: There are mainly three methods to measure the drivers’ driving workload; the subjective measures using questionnaire, driving performance measures using vehicle information, and drivers’ physiological measures using physiological sensors. However, it is not easy to extract drivers’ characteristics and drivers’ driving attitudes even though if we use above three methods to measure drivers’ driving workload. To overcome these limitations, We had developed the DWPT based on these drivers’ characteristics and attitudes as the part of HVI(Human-Vehicle Interface) project. Method: The total of 27 drivers(male 15, female 12) participated in this study. This experiment was conducted using the FOT(Field Operational Test) method that participants are asked to drive pre-defined path on the real local road. EEG data were collected from the participant while driving. Also, participants are asked to answer the DWPT questionnaire. We had analyzed the collected data in two driving scenarios; curve negotiation and straight road. Results: As the result of correlation analysis, DWPT was not correlated with EEG signal on the straight road. However, the relations between DWPT and EEG signal had positive correlation significantly on curve negotiation. In sub-factor analysis, inadaptability of road circumstances was correlated with EEG data. Conclusion: These results suggested that DWPT developed by ETRI had determined and predicted drivers’ real driving workload on curve negotiation. Application: The DWPT is going to be applied to driving Workload Management System and driver adaptive intelligent Human-Vehicle Interface system as a sub-module in the future intelligent car.

The Study on the Prediction of Driving-Workload Using the DWPT in Curve Section: Local Road and Urban Road

( Yoonsook Hwang ),( Daesub Yoon ),( Hyunsuk Kim ),( Hyunsuk Kim ),( Kyong Ho Kim ) 한국감성과학회 2014 춘계학술대회 Vol.2014 No.-

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 thedata 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.

Relationship between Drivers` Characteristics and Risk Perception

( Yoonsook Hwang ),( Byoung Jun Park ),( Kyong Ho Kim ) 한국감성과학회 2015 추계학술대회 Vol.2015 No.-

This study is to confirm the relationship between the drivers’ characteristics and risk perception. Thirty male drivers were participated in this study. We used the DBD questionnaire to collect the psychological characteristics and attitudes of the drivers. The DBD consists of five factors: the Problem Evading, the Benefits/Sensation Seeking, the Anti-personal Anxiety, the Anti-personal Angry, and Aggression. And, we conducted an experiment to collect the risk perception of the drivers using a video clip collected from the real traffic environment and real test vehicle. This video clip contained the experimental stimuli such as risk of a rear-end collision with a preceding vehicle, risk of a collision with a pedestrians on a crosswalk, and risk of a collision with a cutoff vehicle. The participants were randomly assigned to an augmented reality head-up display system usage condition and a control condition. Differences in demographic variables were not significant between the two groups. We conducted a correlation analysis to confirm the relationship between the characteristics of DBD and the risk perception for each condition. As a result, the relationship between the missing rate for risk perception and a benefit/sensation seeking had significant positive correlation only under the AR-HUD condition(r = 0.625, p < 0.05). This suggests that there is a possibility that the drivers with high thisfactor were perceived the augmented reality information about the vehicle and pedestrians as a fun factor. Therefore, the engineers and the designers should be able to apply the human-vehicle interaction technology considering the drivers’ psychological characteristics, attitudes, emotion, feelings to develop a presentation method of an augmented-reality head-up display system.

The study on the prediction of driving-workload using the DWPT in curve section

Yoonsook Hwang,Daesub Yoon,Hyunsuk Kim,Kyong-Ho Kim 대한인간공학회 2014 대한인간공학회 학술대회논문집 Vol.2014 No.5

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.

차량용 증강현실 헤드업디스플레이 시스템 사용이 운전자의 운전행동에 미치는 영향

황윤숙(Yoonsook Hwang),박병준(Byoung-Jun Park),김경호(Kyong-Ho Kim) 대한전자공학회 2015 대한전자공학회 학술대회 Vol.2015 No.6

운전자의 운전행동결정요인과 교통사고 위험의 관계에 관한 연구

황윤숙(Hwang Yoonsook),김도현(Kim Dohyun),장병태(Jang Byongtae),최현균(Choi Hyun Kyun) 한국통신학회 2018 한국통신학회 학술대회논문집 Vol.2018 No.6

조선소 작업장 안전 관리 시스템의 고도화 방안

황윤숙(Hwang Yoonsook),정우성(Jung Woo-Sung),유대승(Yoo Dae Seung) 한국통신학회 2023 한국통신학회 학술대회논문집 Vol.2023 No.6

조선소 작업자 개인 및 작업환경적인 요인을 고려한 안전예측을 위한 연구설계

황윤숙(Hwang Yoonsook),정우성(Jung Woo-Sung),유대승(Yoo Dae Seung) 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.11

조선산업현장에서의 사고는 자칫 큰 사고로 이어질 수 있다. 따라서 조선소 현장이라는 특수성에 맞춰 작업자의 안전을 도모하기 위해 수행된 다양한 연구들을 각 연구의 관점에 따라 분류하여 간략하게 살펴보았다. 그 결과, 조선소 작업자들의 안전을 위해 작업현장의 공간적인 측면 또는 작업자의 행동적인 측면에 초점을 두고 수행된 연구들이 다수 존재하였다. 이는 조선소라는 공간에서 작업자가 행동함으로써 발생할 수 있는 상호작용에 관한 측면, 업무 스트레스라는 심리적인 측면을 함께 고려할 여지가 있음을 시사한다고 볼 수 있다. 이를 기반으로 조선소 작업자의 안전예측모델을 고도화하기 위한 한 방안으로 작업자 개인 및 작업환경적 요인에 관한 정보수집의 방법에 대해 간략하게 설명하고자 한다.