A Study on Re-Engagement and Stabilization Time on Take-Over Transition in a Highly Automated Driving System
In the case of level 3 automated vehicles, in order to safely and quickly transfer control authority rights to manual driving, it is necessary that a study be conducted on the characteristics of human factors affecting the transition of manual driving. In this study, we conducted three experiments t...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/10/3/344 |
| _version_ | 1797416886916349952 |
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| author | Hyunsuk Kim Woojin Kim Jungsook Kim Seung-Jun Lee Daesub Yoon Junghee Jo |
| author_facet | Hyunsuk Kim Woojin Kim Jungsook Kim Seung-Jun Lee Daesub Yoon Junghee Jo |
| author_sort | Hyunsuk Kim |
| collection | DOAJ |
| description | In the case of level 3 automated vehicles, in order to safely and quickly transfer control authority rights to manual driving, it is necessary that a study be conducted on the characteristics of human factors affecting the transition of manual driving. In this study, we conducted three experiments to compare the characteristics of human factors that influence the driver’s quality of response when re-engaging and stabilizing manual driving. The three experiments were conducted sequentially by dividing them into a normal driving situation, an obstacle occurrence situation in front, and an obstacle and congestion on surrounding roads. We performed a statistical analysis and classification and regression tree (CART) analysis using experimental data. We found that as the number of trials increased, there was a learning effect that shortened re-engagement times and increased the proportion of drivers with good response times. We found that the stabilization time increased as the experiment progressed, as obstacles appeared in front and traffic density increased in the surrounding lanes. The results of the analysis are useful for vehicle developers designing safer human–machine interfaces and for governments developing guidelines for automated driving systems. |
| first_indexed | 2024-03-09T06:10:47Z |
| format | Article |
| id | doaj.art-b325418ed923413f9cfc7ce006b80bd3 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T06:10:47Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-b325418ed923413f9cfc7ce006b80bd32023-12-03T11:59:14ZengMDPI AGElectronics2079-92922021-02-0110334410.3390/electronics10030344A Study on Re-Engagement and Stabilization Time on Take-Over Transition in a Highly Automated Driving SystemHyunsuk Kim0Woojin Kim1Jungsook Kim2Seung-Jun Lee3Daesub Yoon4Junghee Jo5Cognition and Transportation ICT Research Section, Electronics and Telecommunications Research Institute, Daejeon 34129, KoreaCognition and Transportation ICT Research Section, Electronics and Telecommunications Research Institute, Daejeon 34129, KoreaCognition and Transportation ICT Research Section, Electronics and Telecommunications Research Institute, Daejeon 34129, KoreaCognition and Transportation ICT Research Section, Electronics and Telecommunications Research Institute, Daejeon 34129, KoreaCognition and Transportation ICT Research Section, Electronics and Telecommunications Research Institute, Daejeon 34129, KoreaDepartment of Computer Education, Busan National University of Education, Busan 47503, KoreaIn the case of level 3 automated vehicles, in order to safely and quickly transfer control authority rights to manual driving, it is necessary that a study be conducted on the characteristics of human factors affecting the transition of manual driving. In this study, we conducted three experiments to compare the characteristics of human factors that influence the driver’s quality of response when re-engaging and stabilizing manual driving. The three experiments were conducted sequentially by dividing them into a normal driving situation, an obstacle occurrence situation in front, and an obstacle and congestion on surrounding roads. We performed a statistical analysis and classification and regression tree (CART) analysis using experimental data. We found that as the number of trials increased, there was a learning effect that shortened re-engagement times and increased the proportion of drivers with good response times. We found that the stabilization time increased as the experiment progressed, as obstacles appeared in front and traffic density increased in the surrounding lanes. The results of the analysis are useful for vehicle developers designing safer human–machine interfaces and for governments developing guidelines for automated driving systems.https://www.mdpi.com/2079-9292/10/3/344automated drivingclassification and regression treecontrol authority transitiontake-over requestre-engagementstabilization |
| spellingShingle | Hyunsuk Kim Woojin Kim Jungsook Kim Seung-Jun Lee Daesub Yoon Junghee Jo A Study on Re-Engagement and Stabilization Time on Take-Over Transition in a Highly Automated Driving System Electronics automated driving classification and regression tree control authority transition take-over request re-engagement stabilization |
| title | A Study on Re-Engagement and Stabilization Time on Take-Over Transition in a Highly Automated Driving System |
| title_full | A Study on Re-Engagement and Stabilization Time on Take-Over Transition in a Highly Automated Driving System |
| title_fullStr | A Study on Re-Engagement and Stabilization Time on Take-Over Transition in a Highly Automated Driving System |
| title_full_unstemmed | A Study on Re-Engagement and Stabilization Time on Take-Over Transition in a Highly Automated Driving System |
| title_short | A Study on Re-Engagement and Stabilization Time on Take-Over Transition in a Highly Automated Driving System |
| title_sort | study on re engagement and stabilization time on take over transition in a highly automated driving system |
| topic | automated driving classification and regression tree control authority transition take-over request re-engagement stabilization |
| url | https://www.mdpi.com/2079-9292/10/3/344 |
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