Driver steering behaviour modelling based on neuromuscular dynamics and multi‑task time‑series transformer
Driver steering intention prediction provides an augmented solution to the design of an onboard collaboration mechanism between human driver and intelligent vehicle. In this study, a multi-task sequential learning framework is developed to predict future steering torques and steering postures based...
| Main Authors: | , , , , , , , |
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| 其他作者: | |
| 格式: | Journal Article |
| 语言: | English |
| 出版: |
2024
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| 主题: | |
| 在线阅读: | https://hdl.handle.net/10356/178713 |
| _version_ | 1826116579351330816 |
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| author | Xing, Yang Hu, Zhongxu Mo, Xiaoyu Hang, Peng Li, Shujing Liu, Yahui Zhao, Yifan Lv, Chen |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Xing, Yang Hu, Zhongxu Mo, Xiaoyu Hang, Peng Li, Shujing Liu, Yahui Zhao, Yifan Lv, Chen |
| author_sort | Xing, Yang |
| collection | NTU |
| description | Driver steering intention prediction provides an augmented solution to the design of an onboard collaboration mechanism between human driver and intelligent vehicle. In this study, a multi-task sequential learning framework is developed to predict future steering torques and steering postures based on upper limb neuromuscular electromyography signals. The joint representation learning for driving postures and steering intention provides an in-depth understanding and accurate modelling of driving steering behaviours. Regarding different testing scenarios, two driving modes, namely, both-hand and single-right-hand modes, are studied. For each driving mode, three different driving postures are further evaluated. Next, a multi-task time-series transformer network (MTS-Trans) is developed to predict the future steering torques and driving postures based on the multi-variate sequential input and the self-attention mechanism. To evaluate the multi-task learning performance and information-sharing characteristics within the network, four distinct two-branch network architectures are evaluated. Empirical validation is conducted through a driving simulator-based experiment, encompassing 21 participants. The proposed model achieves accurate prediction results on future steering torque prediction as well as driving posture recognition for both two-hand and single-hand driving modes. These findings hold significant promise for the advancement of driver steering assistance systems, fostering mutual comprehension and synergy between human drivers and intelligent vehicles. |
| first_indexed | 2024-10-01T04:13:45Z |
| format | Journal Article |
| id | ntu-10356/178713 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:13:45Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1787132024-07-06T16:48:03Z Driver steering behaviour modelling based on neuromuscular dynamics and multi‑task time‑series transformer Xing, Yang Hu, Zhongxu Mo, Xiaoyu Hang, Peng Li, Shujing Liu, Yahui Zhao, Yifan Lv, Chen School of Mechanical and Aerospace Engineering Engineering Driver steering behaviours Neuromuscular dynamics Driver steering intention prediction provides an augmented solution to the design of an onboard collaboration mechanism between human driver and intelligent vehicle. In this study, a multi-task sequential learning framework is developed to predict future steering torques and steering postures based on upper limb neuromuscular electromyography signals. The joint representation learning for driving postures and steering intention provides an in-depth understanding and accurate modelling of driving steering behaviours. Regarding different testing scenarios, two driving modes, namely, both-hand and single-right-hand modes, are studied. For each driving mode, three different driving postures are further evaluated. Next, a multi-task time-series transformer network (MTS-Trans) is developed to predict the future steering torques and driving postures based on the multi-variate sequential input and the self-attention mechanism. To evaluate the multi-task learning performance and information-sharing characteristics within the network, four distinct two-branch network architectures are evaluated. Empirical validation is conducted through a driving simulator-based experiment, encompassing 21 participants. The proposed model achieves accurate prediction results on future steering torque prediction as well as driving posture recognition for both two-hand and single-hand driving modes. These findings hold significant promise for the advancement of driver steering assistance systems, fostering mutual comprehension and synergy between human drivers and intelligent vehicles. Published version 2024-07-03T02:36:24Z 2024-07-03T02:36:24Z 2024 Journal Article Xing, Y., Hu, Z., Mo, X., Hang, P., Li, S., Liu, Y., Zhao, Y. & Lv, C. (2024). Driver steering behaviour modelling based on neuromuscular dynamics and multi‑task time‑series transformer. Automotive Innovation, 7(1), 45-58. https://dx.doi.org/10.1007/s42154-023-00272-x 2096-4250 https://hdl.handle.net/10356/178713 10.1007/s42154-023-00272-x 2-s2.0-85182200801 1 7 45 58 en Automotive Innovation © The Author(s) 2024. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. application/pdf |
| spellingShingle | Engineering Driver steering behaviours Neuromuscular dynamics Xing, Yang Hu, Zhongxu Mo, Xiaoyu Hang, Peng Li, Shujing Liu, Yahui Zhao, Yifan Lv, Chen Driver steering behaviour modelling based on neuromuscular dynamics and multi‑task time‑series transformer |
| title | Driver steering behaviour modelling based on neuromuscular dynamics and multi‑task time‑series transformer |
| title_full | Driver steering behaviour modelling based on neuromuscular dynamics and multi‑task time‑series transformer |
| title_fullStr | Driver steering behaviour modelling based on neuromuscular dynamics and multi‑task time‑series transformer |
| title_full_unstemmed | Driver steering behaviour modelling based on neuromuscular dynamics and multi‑task time‑series transformer |
| title_short | Driver steering behaviour modelling based on neuromuscular dynamics and multi‑task time‑series transformer |
| title_sort | driver steering behaviour modelling based on neuromuscular dynamics and multi task time series transformer |
| topic | Engineering Driver steering behaviours Neuromuscular dynamics |
| url | https://hdl.handle.net/10356/178713 |
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