Title: Large models for fatigue driving detection in future vehicles: a predictive analytics with ensemble neural networks

Authors: Guangwu Hu; Tan Chen; Wei Liu; Yan Li; Dandan Hu

Addresses: School of Computer and Software, Shenzhen University of Information Technology, Guangdong Province, Shenzhen, Guangdong, China ' College of Computer Science, Beijing University of Technology, Beijing, China ' School of Computer and Software, Shenzhen University of Information Technology, Guangdong Province, Shenzhen, Guangdong, China ' School of Computer and Software, Shenzhen University of Information Technology, Guangdong Province, Shenzhen, Guangdong, China ' Beijing Normal-Hong Kong Baptist University, 2000 Jintong Road, Tangjiawan, Zhuhai, Guangdong Province, China

Abstract: In this paper, we introduce a system for fatigue driving detection via analysing spatial-temporal electroencephalogram (EEG) features and employing ensemble neural networks. We extract time-domain EEG features and spatial-domain metric features related to the driving process from EEG data and brain functional network (BFN) data over time. To effectively utilise these features, we develop a feature contribution algorithm that assigns varying contribution coefficients to the time-domain EEG features and the spatial-domain BFN features based on their relationship with the target class. Subsequently, we utilise two sets of weighted features as inputs for two different neural networks: the long short-term memory (LSTM) network and the pseudo three-dimensional convolutional neural network, allowing to harness the complementary information of spatial-temporal EEG features and the data processing capabilities of these two neural network algorithms. Experimental results corroborate the superior performance of the proposed ensemble neural network model compared with the state-of-the-art methods.

Keywords: large models; fatigue driving detection; ensemble neural networks; long short-term memory; LSTM; electroencephalogram; EEG.

DOI: 10.1504/IJAHUC.2026.151265

International Journal of Ad Hoc and Ubiquitous Computing, 2026 Vol.51 No.1, pp.56 - 69

Received: 22 May 2024
Accepted: 09 Aug 2024
Published online: 20 Jan 2026 *

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