  Automated Driving System (ADS) has attracted increasing attention from both
industrial and academic communities due to its potential for increasing the
safety, mobility and efficiency of existing transportation systems. The
state-of-the-art ADS follows the human-in-the-loop (HITL) design, where the
driver's anomalous behaviour is closely monitored by the system. Though many
approaches have been proposed for detecting driver fatigue, they largely depend
on vehicle driving parameters and facial features, which lacks reliability.
Approaches using physiological based sensors (e.g., electroencephalogram or
electrocardiogram) are either too clumsy to wear or impractical to install. In
this paper, we propose a novel driver fatigue detection method by embedding
surface electromyography (sEMG) sensors on a steering wheel. Compared with the
existing methods, our approach is able to collect bio-signals in a
non-intrusive way and detect driver fatigue at an earlier stage. The
experimental results show that our approach outperforms existing methods with
the weighted average F1 scores about 90%. We also propose promising future
directions to deploy this approach in real-life settings, such as applying
multimodal learning using several supplementary sensors.
