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 

机构地区：[1]School of Aerospace,Transport and Manufacturing,Cranfield University,Cranfield MK430AL,United Kingdom [2]School of Mechanical and Aerospace Engineering,Nanyang Technological University,Singapore 639798,Singapore [3]College of Transportation Engineering,Tongji University,Shanghai 200092,China [4]College of Computer Science and Technology,Jilin University,Changchun 130012,China [5]School of Vehicle and Mobility,Tsinghua University,Beijing 100084,China

出　　处：《Automotive Innovation》2024年第1期45-58,共14页汽车创新工程（英文）

摘　　要：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 pre-dict 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 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 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.

关 键 词：Driver steering behaviours Neuromuscular dynamics Multi-task learning Sequential transformer Intelligent vehicles 

分 类 号：U46[机械工程—车辆工程]