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Page 237 Li et al. Intell Robot 2024;4(3):230-43 I http://dx.doi.org/10.20517/ir.2024.15

Fully connected
Input layer GRU layer GRU layer Attention layer layer Output layer

x0 h0 h0 h0
o0
a0
x1 h1 h1 h1 a1
a2 SoftMax
o1
x2 h2 h2 h2
ai
o2
...
...
...
...
xi hi hi hi
Figure 5. Structure of Attention-GRU network model. GRU: Gated recurrent unit.

Figure 6. Experimental driving path.

3. RESULTS
3.1 Experimental data
In this paper, the experimental data sampling path is a 270 km long section of Beijing-Harbin Expressway
from Beijing to Qinhuangdao [Figure 6]. The number of participants is 8, the acquisition time is 1-3 h, and
the acquisition frequency is 100 Hz. The collected data are sliced according to the standard of about 1 min,
and the consistency of the sliced driving behavior data with the driver’s facial video is determined according
to the synchronization pulse signal. Each segment was scored as 0 (awake), 1 (fatigued) and 2 (very fatigued)
according to the driver’s facial fatigue score. A new fatigue driving sample dataset was obtained. The vehicle
speed below 60 km/h in the dataset is considered as indicating slow sections, and the steering wheel turning
angle over 20° is considered as indicative of overtaking lane change. There are 243 sober samples, 71 fatigue
samples and 30 very fatigue samples after excluding these abnormal data, totaling 237 samples. Considering
the unbalanced and too few samples of the three-level fatigue samples, the smote method was used to expand
171 fatigue samples and 212 very fatigue samples, each containing 12 dimensions and 6,000 lines of operation
behavior data.

3.2 Data analysis
The fatigue state induces psychological and physiological changes in the driver, leading to a decrease in the
driver’s control accuracy over the vehicle and subsequent abnormal operating behaviors. Consequently, mon-
itoring indicators related to driving operation behaviors allows for a real-time assessment of the driver’s state.
The SWA , being the device most directly manipulated by the driver, is also the most frequently operated. The
data is illustrated in Figure 7 [24] . In the awake state, the SWA exhibits frequent fluctuations with a small am-
plitude. In the fatigue state, the fluctuation amplitude increases, and in a very fatigued state, the SWA may
show stationary motion with significant fluctuations. The driver modulates vehicle speed through the throttle