A comparative study of speculative retrieval for multi-modal data trails: towards user-friendly Human-Vehicle interactions
In the era of growing developments in Autonomous Vehicles, the importance of Human-Vehicle Interaction has become apparent. However, the requirements of retrieving in-vehicle drivers’ multi- modal data trails, by utilizing embedded sensors, have been consid- ered user unfriendly and impractical. Hen...
| Main Authors: | , , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/60660/ |
| _version_ | 1848799790581678080 |
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| author | Wang, Yaohua Huang, Zhentao Li, Rongze Zhang, Zheng Sun, Xu Yin, Xinyu Luo, Min |
| author_facet | Wang, Yaohua Huang, Zhentao Li, Rongze Zhang, Zheng Sun, Xu Yin, Xinyu Luo, Min |
| author_sort | Wang, Yaohua |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | In the era of growing developments in Autonomous Vehicles, the importance of Human-Vehicle Interaction has become apparent. However, the requirements of retrieving in-vehicle drivers’ multi- modal data trails, by utilizing embedded sensors, have been consid- ered user unfriendly and impractical. Hence, speculative designs, for in-vehicle multi-modal data retrieval, has been demanded for future personalized and intelligent Human-Vehicle Interaction. In this paper, we explore the feasibility to utilize facial recog- nition techniques to build in-vehicle multi-modal data retrieval. We first perform a comprehensive user study to collect relevant data and extra trails through sensors, cameras and questionnaire. Then, we build the whole pipeline through Convolution Neural Net- works to predict multi-model values of three particular categories of data, which are Heart Rate, Skin Conductance and Vehicle Speed, by solely taking facial expressions as input. We further evaluate and validate its effectiveness within the data set, which suggest the promising future of Speculative Designs for Multi-modal Data Retrieval through this approach. |
| first_indexed | 2025-11-14T20:41:16Z |
| format | Conference or Workshop Item |
| id | nottingham-60660 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:41:16Z |
| publishDate | 2020 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-606602020-05-21T05:41:14Z https://eprints.nottingham.ac.uk/60660/ A comparative study of speculative retrieval for multi-modal data trails: towards user-friendly Human-Vehicle interactions Wang, Yaohua Huang, Zhentao Li, Rongze Zhang, Zheng Sun, Xu Yin, Xinyu Luo, Min In the era of growing developments in Autonomous Vehicles, the importance of Human-Vehicle Interaction has become apparent. However, the requirements of retrieving in-vehicle drivers’ multi- modal data trails, by utilizing embedded sensors, have been consid- ered user unfriendly and impractical. Hence, speculative designs, for in-vehicle multi-modal data retrieval, has been demanded for future personalized and intelligent Human-Vehicle Interaction. In this paper, we explore the feasibility to utilize facial recog- nition techniques to build in-vehicle multi-modal data retrieval. We first perform a comprehensive user study to collect relevant data and extra trails through sensors, cameras and questionnaire. Then, we build the whole pipeline through Convolution Neural Net- works to predict multi-model values of three particular categories of data, which are Heart Rate, Skin Conductance and Vehicle Speed, by solely taking facial expressions as input. We further evaluate and validate its effectiveness within the data set, which suggest the promising future of Speculative Designs for Multi-modal Data Retrieval through this approach. 2020-12-01 Conference or Workshop Item PeerReviewed application/pdf en cc_by https://eprints.nottingham.ac.uk/60660/7/%E2%88%9AA%20Comparative%20Study%20of%20Speculative%20Retrieval%20for%20Multi-modal%20Data%20Trails%20Towards%20User-friendly%20Human-Vehicle%20Interactions.pdf Wang, Yaohua, Huang, Zhentao, Li, Rongze, Zhang, Zheng, Sun, Xu, Yin, Xinyu and Luo, Min (2020) A comparative study of speculative retrieval for multi-modal data trails: towards user-friendly Human-Vehicle interactions. In: ICCAI 2020 6th International Conference on Computing and Artificial Intelligence, April 23-26, 2020, Tianjin, China. (In Press) Human-Vehicle Interaction; Facial Recognition; Multi-modal Data Streams |
| spellingShingle | Human-Vehicle Interaction; Facial Recognition; Multi-modal Data Streams Wang, Yaohua Huang, Zhentao Li, Rongze Zhang, Zheng Sun, Xu Yin, Xinyu Luo, Min A comparative study of speculative retrieval for multi-modal data trails: towards user-friendly Human-Vehicle interactions |
| title | A comparative study of speculative retrieval for multi-modal data trails: towards user-friendly Human-Vehicle interactions |
| title_full | A comparative study of speculative retrieval for multi-modal data trails: towards user-friendly Human-Vehicle interactions |
| title_fullStr | A comparative study of speculative retrieval for multi-modal data trails: towards user-friendly Human-Vehicle interactions |
| title_full_unstemmed | A comparative study of speculative retrieval for multi-modal data trails: towards user-friendly Human-Vehicle interactions |
| title_short | A comparative study of speculative retrieval for multi-modal data trails: towards user-friendly Human-Vehicle interactions |
| title_sort | comparative study of speculative retrieval for multi-modal data trails: towards user-friendly human-vehicle interactions |
| topic | Human-Vehicle Interaction; Facial Recognition; Multi-modal Data Streams |
| url | https://eprints.nottingham.ac.uk/60660/ |