Generative AI and Attentive User Interfaces: Five Strategies to Enhance Take-Over Quality in Automated Driving
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arXiv
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916128389857280 |
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| author | Ebel, Patrick |
| author_facet | Ebel, Patrick |
| contents | As the automotive world moves toward higher levels of driving automation, Level 3 automated driving represents a critical juncture. In Level 3 driving, vehicles can drive alone under limited conditions, but drivers are expected to be ready to take over when the system requests. Assisting the driver to maintain an appropriate level of Situation Awareness (SA) in such contexts becomes a critical task. This position paper explores the potential of Attentive User Interfaces (AUIs) powered by generative Artificial Intelligence (AI) to address this need. Rather than relying on overt notifications, we argue that AUIs based on novel AI technologies such as large language models or diffusion models can be used to improve SA in an unconscious and subtle way without negative effects on drivers overall workload. Accordingly, we propose 5 strategies how generative AI s can be used to improve the quality of takeovers and, ultimately, road safety. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_10664 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generative AI and Attentive User Interfaces: Five Strategies to Enhance Take-Over Quality in Automated Driving Ebel, Patrick Human-Computer Interaction As the automotive world moves toward higher levels of driving automation, Level 3 automated driving represents a critical juncture. In Level 3 driving, vehicles can drive alone under limited conditions, but drivers are expected to be ready to take over when the system requests. Assisting the driver to maintain an appropriate level of Situation Awareness (SA) in such contexts becomes a critical task. This position paper explores the potential of Attentive User Interfaces (AUIs) powered by generative Artificial Intelligence (AI) to address this need. Rather than relying on overt notifications, we argue that AUIs based on novel AI technologies such as large language models or diffusion models can be used to improve SA in an unconscious and subtle way without negative effects on drivers overall workload. Accordingly, we propose 5 strategies how generative AI s can be used to improve the quality of takeovers and, ultimately, road safety. |
| title | Generative AI and Attentive User Interfaces: Five Strategies to Enhance Take-Over Quality in Automated Driving |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2402.10664 |