Page 181 - Read Online

Page 241 Li et al. Intell Robot 2024;4(3):230-43 I http://dx.doi.org/10.20517/ir.2024.15

Table 3. Evaluation results of the WOA-Attention-GRU fatigue driving detection model (adapted from Li et al., 2023 [24] )
Type of sample Precision Recall Condition positive F1-score
Awake 94.44% 79.07% 20.13% 86.07%
Fatigued 81.63% 88.89% 11.11% 85.11%
Very fatigued 93.44% 98.28% 1.72% 95.80%
Overall percentages 89.84% 88.75% 10.99% 88.99%
WOA: Whale optimization algorithm; GRU: gated recurrent unit.

c. Underreporting rate:

= 1 − (19)
+ +
d. F1-score:
2 · ·
1 = (20)
+

Applying the definitions given in the equation above, the evaluation results for the Attention-GRU fatigue
driving detection model, optimized by the whale algorithm in this research, are presented in Table 3 [24] . The
model achieves an accuracy rate of 89.84%, a recall rate of 88.77%, a miss rate of 10.99%, and an F1-score of
88.99%.

4. DISCUSSION
This study presents a fatigue driving recognition method based on a WOA-enhanced Attention-GRU model.
AfteroptimizationthroughtheWOA,theoverallrecognitionaccuracyoftheAttention-GRUmodelforfatigue
driving reaches 89.84%. This represents a 6% improvement over the non-optimized Attention-GRU model, a
14% enhancement over the GRU model, and approximately an 11% increase compared to fatigue driving de-
tection methods that focus solely on the real vehicle steering angle. The missed detection rate is 10.99%. The
proposed fatigue driving recognition method utilizes real car driving operation data, which enhances its prac-
tical engineering applicability. However, this study does not account for individual driver differences. In
future research, it is imperative to expand the fatigue driving sample database and explore the variations in
operational behavior among different drivers to improve the robustness and generalizability of the fatigue driv-
ing recognition model. Additionally, to enhance the model’s performance in long-term monitoring scenarios,
more extensive studies are planned to investigate how drivers adapt to the fatigue monitoring system over time,
tracking behavioral changes post-implementation.

5. CONCLUSIONS
In this paper, we developed a fatigue driving recognition model, WOA-Attention-GRU, which demonstrated
promising results in detecting various states of driver fatigue. The model was validated using real measured
data, ensuring its reliability and relevance to practical driving scenarios. However, we acknowledge that the
generalizability of our findings can be further enhanced by testing the model on larger datasets. Future work
will involve collecting more extensive data for further verification and validation of the proposed method to
ensure robustness and wider applicability across different driving scenarios. Furthermore, acknowledging the
importance of addressing long-term monitoring challenges, we plan to update and improve the monitoring
system based on actual usage feedback, ensuring that it can adapt to evolving driver needs and behaviors.
Special algorithm adjustments or model updates may be necessary to address time-related changes effectively.