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Li et al. Intell Robot 2024;4(3):230-43 Intelligence & Robotics
DOI: 10.20517/ir.2024.15

Research Article Open Access

A novel fatigue driving detection method based on
whale optimization and Attention-enhanced GRU

1
2
Zuojin Li 1 , Minghong Li , Lanyang Shi , Dongyang Li 1
1 School of Electrical Engineering, Chongqing University of Science and Technology, Chongqing 401331, China.
2 Power and Test Calibration Department, Qingling Motors Co. Ltd, Chongqing 401331, China.

Correspondence to: Prof. Zuojin Li, School of Electrical Engineering, Chongqing University of Science and Technology, Room 417-2,
Block I, Yifu Building, Chongqing 401331, China. E-mail: cqustlzj@sina.cn

How to cite this article: Li Z, Li M, Shi L, Li D. A novel fatigue driving detection method based on whale optimization and Attention-
enhanced GRU. Intell Robot 2024;4(3):230-43. http://dx.doi.org/10.20517/ir.2024.15
Received: 25 Feb 2024 First Decision: 29 May 2024 Revised: 18 Jul 2024 Accepted: 24 Jul 2024 Published: 30 Jul 2024

Academic Editors: Simon X. Yang, Lei Lei Copy Editor: Pei-Yun Wang Production Editor: Pei-Yun Wang

Abstract

Fatigue driving has emerged as the predominant causative factor for road traffic safety accidents. The fatigue driving
detection method, derived from laboratory simulation data, faces challenges related to imbalanced data distribution
and limited recognition accuracy in practical scenarios. In this study, we introduce a novel approach utilizing a gated
recurrent neural network method, employing whale optimization algorithm for fatigue driving identification. Addi-
tionally, we incorporate an attention mechanism to enhance identification accuracy. Initially, this study focuses on
the driver’s operational behavior under authentic vehicular conditions. Subsequently, it employs wavelet energy en-
tropy, scale entropy, and singular entropy analysis to extract the fatigue-related features from the driver’s operational
behavior. Subsequently, this study adopts the cross-validation recursive feature elimination method to derive the op-
timal fatigue feature index about operational behavior. To effectively capture long-range dependence relationships,
this study employs the gated recurrent unit neural network method. Lastly, an attention mechanism is incorporated
in this study to concentrate on pivotal features within the data sequence of driving behavior. It assigns greater weight
to crucial information, mitigating information loss caused by the extended temporal sequence. Experimental results
obtained from real vehicle data demonstrate that the proposed method achieves an accuracy of 89.84% in third-
level fatigue driving detection, with an omission rate of 10.99%. These findings affirm the feasibility of the approach
presented in this study.

Keywords: Traffic safety, fatigue driving, operational behavior, whale optimization, neural network

© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0
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