Clustering and Investigation of Human Driving Behavior Using Autoencoder and Risk Assessment
This paper introduces a novel methodology for evaluating human driving behavior influenced by shoe type and its impact on collision risk. While human factors, such as footwear, are recognized to affect driving safety, studies quantitively assessing the effects of shoe types on safety has been limite...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10843227/ |
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| author | Donghoon Shin Jinhee Myoung Woongsun Jeon Kang-Moon Park |
| author_facet | Donghoon Shin Jinhee Myoung Woongsun Jeon Kang-Moon Park |
| author_sort | Donghoon Shin |
| collection | DOAJ |
| description | This paper introduces a novel methodology for evaluating human driving behavior influenced by shoe type and its impact on collision risk. While human factors, such as footwear, are recognized to affect driving safety, studies quantitively assessing the effects of shoe types on safety has been limited. To address this, we utilize an autoencoder and human-centered risk assessment algorithms to investigate human driving behavior and collision risk. Experiments were conducted in various real-world driving scenarios, involving two distinct types of shoes. The autoencoder extracts features from the driving data and enables us to analyze the effects of shoe type on driving behavior. Additionally, collision risk analysis is used to verify the validity and impact of the feature extraction results on safe driving. This study contributes to enhancing our understanding of how footwear influences driver behavior and safety. Furthermore, this methodology establishes a groundwork for future research on applying quantitative evaluations to other human factors that influence driving behavior. |
| format | Article |
| id | doaj-art-5fcf9eaae3164c19a5df2f0344a37eb8 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-5fcf9eaae3164c19a5df2f0344a37eb82025-01-28T00:01:09ZengIEEEIEEE Access2169-35362025-01-0113128321284510.1109/ACCESS.2025.352988310843227Clustering and Investigation of Human Driving Behavior Using Autoencoder and Risk AssessmentDonghoon Shin0https://orcid.org/0000-0002-6280-7550Jinhee Myoung1Woongsun Jeon2https://orcid.org/0000-0003-1668-8893Kang-Moon Park3https://orcid.org/0000-0003-2452-9438Division of Artificial Intelligence Engineering, Korea Maritime & Ocean University, Busan, Republic of KoreaAutonomous Driving Engineering Team, Hyundai Motor Company, Seoul, Republic of KoreaSchool of Electrical and Electronics Engineering, Chung-Ang University, Seoul, Republic of KoreaDepartment of Electronic Engineering, Korea National University of Transportation, Chungju-si, Chungcheongbuk-do, Republic of KoreaThis paper introduces a novel methodology for evaluating human driving behavior influenced by shoe type and its impact on collision risk. While human factors, such as footwear, are recognized to affect driving safety, studies quantitively assessing the effects of shoe types on safety has been limited. To address this, we utilize an autoencoder and human-centered risk assessment algorithms to investigate human driving behavior and collision risk. Experiments were conducted in various real-world driving scenarios, involving two distinct types of shoes. The autoencoder extracts features from the driving data and enables us to analyze the effects of shoe type on driving behavior. Additionally, collision risk analysis is used to verify the validity and impact of the feature extraction results on safe driving. This study contributes to enhancing our understanding of how footwear influences driver behavior and safety. Furthermore, this methodology establishes a groundwork for future research on applying quantitative evaluations to other human factors that influence driving behavior.https://ieeexplore.ieee.org/document/10843227/Autonomous drivingautoencoderdriving behaviordeep learningrisk assessment |
| spellingShingle | Donghoon Shin Jinhee Myoung Woongsun Jeon Kang-Moon Park Clustering and Investigation of Human Driving Behavior Using Autoencoder and Risk Assessment IEEE Access Autonomous driving autoencoder driving behavior deep learning risk assessment |
| title | Clustering and Investigation of Human Driving Behavior Using Autoencoder and Risk Assessment |
| title_full | Clustering and Investigation of Human Driving Behavior Using Autoencoder and Risk Assessment |
| title_fullStr | Clustering and Investigation of Human Driving Behavior Using Autoencoder and Risk Assessment |
| title_full_unstemmed | Clustering and Investigation of Human Driving Behavior Using Autoencoder and Risk Assessment |
| title_short | Clustering and Investigation of Human Driving Behavior Using Autoencoder and Risk Assessment |
| title_sort | clustering and investigation of human driving behavior using autoencoder and risk assessment |
| topic | Autonomous driving autoencoder driving behavior deep learning risk assessment |
| url | https://ieeexplore.ieee.org/document/10843227/ |
| work_keys_str_mv | AT donghoonshin clusteringandinvestigationofhumandrivingbehaviorusingautoencoderandriskassessment AT jinheemyoung clusteringandinvestigationofhumandrivingbehaviorusingautoencoderandriskassessment AT woongsunjeon clusteringandinvestigationofhumandrivingbehaviorusingautoencoderandriskassessment AT kangmoonpark clusteringandinvestigationofhumandrivingbehaviorusingautoencoderandriskassessment |