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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/2412.04888 |
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| _version_ | 1866913600043483136 |
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| author | Wang, Jie Cai, Mobing Zhu, Zhongpan Ding, Hongjun Yi, Jiwei Du, Aimin |
| author_facet | Wang, Jie Cai, Mobing Zhu, Zhongpan Ding, Hongjun Yi, Jiwei Du, Aimin |
| contents | In the domain of autonomous vehicles, the human-vehicle co-pilot system has garnered significant research attention. To address the subjective uncertainties in driver state and interaction behaviors, which are pivotal to the safety of Human-in-the-loop co-driving systems, we introduce a novel visual-tactile perception method. Utilizing a driving simulation platform, a comprehensive dataset has been developed that encompasses multi-modal data under fatigue and distraction conditions. The experimental setup integrates driving simulation with signal acquisition, yielding 600 minutes of fatigue detection data from 15 subjects and 102 takeover experiments with 17 drivers. The dataset, synchronized across modalities, serves as a robust resource for advancing cross-modal driver behavior perception algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_04888 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | VTD: Visual and Tactile Database for Driver State and Behavior Perception Wang, Jie Cai, Mobing Zhu, Zhongpan Ding, Hongjun Yi, Jiwei Du, Aimin Robotics Artificial Intelligence In the domain of autonomous vehicles, the human-vehicle co-pilot system has garnered significant research attention. To address the subjective uncertainties in driver state and interaction behaviors, which are pivotal to the safety of Human-in-the-loop co-driving systems, we introduce a novel visual-tactile perception method. Utilizing a driving simulation platform, a comprehensive dataset has been developed that encompasses multi-modal data under fatigue and distraction conditions. The experimental setup integrates driving simulation with signal acquisition, yielding 600 minutes of fatigue detection data from 15 subjects and 102 takeover experiments with 17 drivers. The dataset, synchronized across modalities, serves as a robust resource for advancing cross-modal driver behavior perception algorithms. |
| title | VTD: Visual and Tactile Database for Driver State and Behavior Perception |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2412.04888 |