Driver Steering Behaviour Modelling Based on Neuromuscular Dynamics and Multi‑Task Time‑Series Transformer

作     者：Yang Xing Zhongxu Hu Xiaoyu Mo Peng Hang Shujing Li Yahui Liu Yifan Zhao Chen Lv 

作者机构：School of AerospaceTransport and ManufacturingCranfield UniversityCranfield MK430ALUnited Kingdom School of Mechanical and Aerospace EngineeringNanyang Technological UniversitySingapore 639798Singapore College of Transportation EngineeringTongji UniversityShanghai 200092China College of Computer Science and TechnologyJilin UniversityChangchun 130012China School of Vehicle and MobilityTsinghua UniversityBeijing 100084China 

出 版 物：《Automotive Innovation》 (汽车创新工程（英文）)

年 卷 期：2024年第7卷第1期

页      面：45-58页

核心收录：

学科分类：082304[工学-载运工具运用工程] 08[工学] 080204[工学-车辆工程] 0802[工学-机械工程] 0823[工学-交通运输工程] 

主　　题：Driver steering behaviours Neuromuscular dynamics Multi-task learning Sequential transformer Intelligent vehicles 

摘      要：Driver steering intention prediction provides an augmented solution to the design of an onboard collaboration mechanism between human driver and intelligent *** this study,a multi-task sequential learning framework is developed to pre-dict future steering torques and steering postures based on upper limb neuromuscular electromyography *** joint representation learning for driving postures and steering intention provides an in-depth understanding and accurate modelling of driving steering *** different testing scenarios,two driving modes,namely,both-hand and single-right-hand modes,are *** each driving mode,three different driving postures are further ***,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 *** evaluate the multi-task learning performance and information-sharing characteristics within the network,four distinct two-branch network architectures are *** validation is conducted through a driving simulator-based experiment,encompassing 21 *** pro-posed model achieves accurate prediction results on future steering torque prediction as well as driving posture recognition for both two-hand and single-hand driving *** findings hold significant promise for the advancement of driver steering assistance systems,fostering mutual comprehension and synergy between human drivers and intelligent vehicles.