  Human intention prediction provides an augmented solution for the design of
assistants and collaboration between the human driver and intelligent vehicles.
In this study, a multi-task sequential learning framework is developed to
predict future steering torques and steering postures based on the upper limb
neuromuscular Electromyography (EMG) signals. A single-right-hand driving mode
is particularly studied. For this driving mode, three different driving
postures are also evaluated. Then, a multi-task time-series transformer network
(MTS-Trans) is developed to predict the steering torques and driving postures.
To evaluate the multi-task learning performance, four different frameworks are
assessed. Twenty-one participants are involved in the driving simulator-based
experiment. The proposed model achieved accurate prediction results on the
future steering torque prediction and driving postures recognition for
single-hand driving modes. The proposed system can contribute to the
development of advanced driver steering assistant systems and ensure mutual
understanding between human drivers and intelligent vehicles.
