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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.03774 |
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| _version_ | 1866916424457388032 |
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| author | Puphal, Tim Flade, Benedict Krüger, Matti Hirano, Ryohei Kimata, Akihito |
| author_facet | Puphal, Tim Flade, Benedict Krüger, Matti Hirano, Ryohei Kimata, Akihito |
| contents | This paper addresses the problem of human-based driver support. Nowadays, driver support systems help users to operate safely in many driving situations. Nevertheless, these systems do not fully use the rich information that is available from sensing the human driver. In this paper, we therefore present a human-based risk model that uses driver information for improved driver support. In contrast to state of the art, our proposed risk model combines a) the current driver perception based on driver errors, such as the driver overlooking another vehicle (i.e., notice error), and b) driver personalization, such as the driver being defensive or confident. In extensive simulations of multiple interactive driving scenarios, we show that our novel human-based risk model achieves earlier warning times and reduced warning errors compared to a baseline risk model not using human driver information. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_03774 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Human-Based Risk Model for Improved Driver Support in Interactive Driving Scenarios Puphal, Tim Flade, Benedict Krüger, Matti Hirano, Ryohei Kimata, Akihito Human-Computer Interaction Artificial Intelligence This paper addresses the problem of human-based driver support. Nowadays, driver support systems help users to operate safely in many driving situations. Nevertheless, these systems do not fully use the rich information that is available from sensing the human driver. In this paper, we therefore present a human-based risk model that uses driver information for improved driver support. In contrast to state of the art, our proposed risk model combines a) the current driver perception based on driver errors, such as the driver overlooking another vehicle (i.e., notice error), and b) driver personalization, such as the driver being defensive or confident. In extensive simulations of multiple interactive driving scenarios, we show that our novel human-based risk model achieves earlier warning times and reduced warning errors compared to a baseline risk model not using human driver information. |
| title | Human-Based Risk Model for Improved Driver Support in Interactive Driving Scenarios |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2410.03774 |