Classification of Automated Lane-Change Styles by Modeling and Analyzing Truck Driver Behavior: A Driving Simulator Study
Lane change is a highly demanding driving task. A number of traffic accidents are induced by erroneous maneuvers. An automated lane-change system has the potential to reduce the driver workload and improve driving safety. A challenge is to improve the driver acceptance of the automated system. From...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Open Journal of Intelligent Transportation Systems |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9953158/ |
| _version_ | 1828084148893384704 |
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| author | Zheng Wang Muhua Guan Jin Lan Bo Yang Tsutomu Kaizuka Junichi Taki Kimihiko Nakano |
| author_facet | Zheng Wang Muhua Guan Jin Lan Bo Yang Tsutomu Kaizuka Junichi Taki Kimihiko Nakano |
| author_sort | Zheng Wang |
| collection | DOAJ |
| description | Lane change is a highly demanding driving task. A number of traffic accidents are induced by erroneous maneuvers. An automated lane-change system has the potential to reduce the driver workload and improve driving safety. A challenge is to improve the driver acceptance of the automated system. From the perspective of human factors, an automated system with different styles would improve user acceptance, because drivers could drive with different styles in different driving scenarios. This paper proposes a method to design different lane-change styles for automated driving by analyzing and modeling truck-driver behavior. A truck driving simulator experiment with 12 participants was conducted to identify the driver-model parameters. The lane change styles were classified into three types: aggressive, medium, and conservative. The proposed automated lane-change system was evaluated by another truck driving simulator experiment with the same 12 participants. Moreover, the effects of different lane-change decisionmaking styles on the driver experience and acceptance were evaluated from the perspectives of both the ego truck and surrounding vehicles. The evaluation results demonstrate that different lane-change decisionmaking styles can be distinguished by drivers. Overall, the three styles were evaluated by the human drivers as being safe and reliable. The main contribution of this study is that it provides the insights into the design of an automated driving system with different driving styles. Furthermore, these observations can be applied to commercial automated trucks. |
| first_indexed | 2024-04-11T04:19:29Z |
| format | Article |
| id | doaj.art-6c4077a6f70240529179eced48e92c59 |
| institution | Directory Open Access Journal |
| issn | 2687-7813 |
| language | English |
| last_indexed | 2024-04-11T04:19:29Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Intelligent Transportation Systems |
| spelling | doaj.art-6c4077a6f70240529179eced48e92c592022-12-31T00:02:10ZengIEEEIEEE Open Journal of Intelligent Transportation Systems2687-78132022-01-01377278510.1109/OJITS.2022.32224429953158Classification of Automated Lane-Change Styles by Modeling and Analyzing Truck Driver Behavior: A Driving Simulator StudyZheng Wang0https://orcid.org/0000-0002-7589-7954Muhua Guan1https://orcid.org/0000-0002-7478-6152Jin Lan2Bo Yang3https://orcid.org/0000-0001-8976-5971Tsutomu Kaizuka4https://orcid.org/0000-0003-1140-5604Junichi Taki5Kimihiko Nakano6https://orcid.org/0000-0003-3532-960XInstitute of Industrial Science, The University of Tokyo, Tokyo, JapanInstitute of Industrial Science, The University of Tokyo, Tokyo, JapanInstitute of Industrial Science, The University of Tokyo, Tokyo, JapanInstitute of Industrial Science, The University of Tokyo, Tokyo, JapanDepartment of Mechanical Science and Engineering, Kogakuin University, Tokyo, JapanVehicle Experiment Department, Hino Motors Ltd., Tokyo, JapanInstitute of Industrial Science, The University of Tokyo, Tokyo, JapanLane change is a highly demanding driving task. A number of traffic accidents are induced by erroneous maneuvers. An automated lane-change system has the potential to reduce the driver workload and improve driving safety. A challenge is to improve the driver acceptance of the automated system. From the perspective of human factors, an automated system with different styles would improve user acceptance, because drivers could drive with different styles in different driving scenarios. This paper proposes a method to design different lane-change styles for automated driving by analyzing and modeling truck-driver behavior. A truck driving simulator experiment with 12 participants was conducted to identify the driver-model parameters. The lane change styles were classified into three types: aggressive, medium, and conservative. The proposed automated lane-change system was evaluated by another truck driving simulator experiment with the same 12 participants. Moreover, the effects of different lane-change decisionmaking styles on the driver experience and acceptance were evaluated from the perspectives of both the ego truck and surrounding vehicles. The evaluation results demonstrate that different lane-change decisionmaking styles can be distinguished by drivers. Overall, the three styles were evaluated by the human drivers as being safe and reliable. The main contribution of this study is that it provides the insights into the design of an automated driving system with different driving styles. Furthermore, these observations can be applied to commercial automated trucks.https://ieeexplore.ieee.org/document/9953158/Intelligent transportation systemshuman factorsautomated drivinghuman–machine systemsdriving styles |
| spellingShingle | Zheng Wang Muhua Guan Jin Lan Bo Yang Tsutomu Kaizuka Junichi Taki Kimihiko Nakano Classification of Automated Lane-Change Styles by Modeling and Analyzing Truck Driver Behavior: A Driving Simulator Study IEEE Open Journal of Intelligent Transportation Systems Intelligent transportation systems human factors automated driving human–machine systems driving styles |
| title | Classification of Automated Lane-Change Styles by Modeling and Analyzing Truck Driver Behavior: A Driving Simulator Study |
| title_full | Classification of Automated Lane-Change Styles by Modeling and Analyzing Truck Driver Behavior: A Driving Simulator Study |
| title_fullStr | Classification of Automated Lane-Change Styles by Modeling and Analyzing Truck Driver Behavior: A Driving Simulator Study |
| title_full_unstemmed | Classification of Automated Lane-Change Styles by Modeling and Analyzing Truck Driver Behavior: A Driving Simulator Study |
| title_short | Classification of Automated Lane-Change Styles by Modeling and Analyzing Truck Driver Behavior: A Driving Simulator Study |
| title_sort | classification of automated lane change styles by modeling and analyzing truck driver behavior a driving simulator study |
| topic | Intelligent transportation systems human factors automated driving human–machine systems driving styles |
| url | https://ieeexplore.ieee.org/document/9953158/ |
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