Objective Detection of Trust in Automated Urban Air Mobility: A Deep Learning-Based ERP Analysis
Urban Air Mobility (UAM) has emerged in response to increasing traffic demands. As UAM involves commercial flights in complex urban areas, well-established automation technologies are critical to ensure a safe, accessible, and reliable flight. However, the current level of acceptance of automation i...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Aerospace |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2226-4310/11/3/174 |
| _version_ | 1797242504837332992 |
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| author | Yuhan Li Shuguang Zhang Ruichen He Florian Holzapfel |
| author_facet | Yuhan Li Shuguang Zhang Ruichen He Florian Holzapfel |
| author_sort | Yuhan Li |
| collection | DOAJ |
| description | Urban Air Mobility (UAM) has emerged in response to increasing traffic demands. As UAM involves commercial flights in complex urban areas, well-established automation technologies are critical to ensure a safe, accessible, and reliable flight. However, the current level of acceptance of automation is insufficient. Therefore, this study sought to objectively detect the degree of human trust toward UAM automation. Electroencephalography (EEG) signals, specifically Event-Related Potentials (ERP), were employed to analyze and detect operators’ trust towards automated UAM, providing insights into cognitive processes related to trust. A two-dimensional convolutional neural network integrated with an attention mechanism (2D-ACNN) was also established to enable the end-to-end detection of trust through EEG signals. The results revealed that our proposed 2D-ACNN outperformed other state-of-the-art methods. This work contributes to enhancing the trustworthiness and popularity of UAM automation, which is essential for the widespread adoption and advances in the UAM domain. |
| first_indexed | 2024-04-24T18:40:17Z |
| format | Article |
| id | doaj.art-ca582e3d83f24691907b41b2ef07a3d4 |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-04-24T18:40:17Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-ca582e3d83f24691907b41b2ef07a3d42024-03-27T13:15:34ZengMDPI AGAerospace2226-43102024-02-0111317410.3390/aerospace11030174Objective Detection of Trust in Automated Urban Air Mobility: A Deep Learning-Based ERP AnalysisYuhan Li0Shuguang Zhang1Ruichen He2Florian Holzapfel3School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, ChinaSchool of Transportation Science and Engineering, Beihang University, Beijing 100191, ChinaInstitute of Flight System Dynamics, Technical University of Munich, 80333 Munich, GermanyInstitute of Flight System Dynamics, Technical University of Munich, 80333 Munich, GermanyUrban Air Mobility (UAM) has emerged in response to increasing traffic demands. As UAM involves commercial flights in complex urban areas, well-established automation technologies are critical to ensure a safe, accessible, and reliable flight. However, the current level of acceptance of automation is insufficient. Therefore, this study sought to objectively detect the degree of human trust toward UAM automation. Electroencephalography (EEG) signals, specifically Event-Related Potentials (ERP), were employed to analyze and detect operators’ trust towards automated UAM, providing insights into cognitive processes related to trust. A two-dimensional convolutional neural network integrated with an attention mechanism (2D-ACNN) was also established to enable the end-to-end detection of trust through EEG signals. The results revealed that our proposed 2D-ACNN outperformed other state-of-the-art methods. This work contributes to enhancing the trustworthiness and popularity of UAM automation, which is essential for the widespread adoption and advances in the UAM domain.https://www.mdpi.com/2226-4310/11/3/174automated vehiclesUAMtrust in automationcognitionevent-related potentialsCNN |
| spellingShingle | Yuhan Li Shuguang Zhang Ruichen He Florian Holzapfel Objective Detection of Trust in Automated Urban Air Mobility: A Deep Learning-Based ERP Analysis Aerospace automated vehicles UAM trust in automation cognition event-related potentials CNN |
| title | Objective Detection of Trust in Automated Urban Air Mobility: A Deep Learning-Based ERP Analysis |
| title_full | Objective Detection of Trust in Automated Urban Air Mobility: A Deep Learning-Based ERP Analysis |
| title_fullStr | Objective Detection of Trust in Automated Urban Air Mobility: A Deep Learning-Based ERP Analysis |
| title_full_unstemmed | Objective Detection of Trust in Automated Urban Air Mobility: A Deep Learning-Based ERP Analysis |
| title_short | Objective Detection of Trust in Automated Urban Air Mobility: A Deep Learning-Based ERP Analysis |
| title_sort | objective detection of trust in automated urban air mobility a deep learning based erp analysis |
| topic | automated vehicles UAM trust in automation cognition event-related potentials CNN |
| url | https://www.mdpi.com/2226-4310/11/3/174 |
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