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Distraction-level recognition based on stacking ensemble learning for IVIS secondary tasks

分散注意力 计算机科学 人工智能 机器学习 自编码 随机森林 分心驾驶 梯度升压 集成学习 多层感知器 人工神经网络 监督学习 决策树 Boosting(机器学习) 无监督学习 模式识别(心理学) 生物 神经科学
作者
Xia Zhao,Li Zhao,Chen Zhao,Rui Fu,Chang Wang
出处
期刊:Expert Systems With Applications [Elsevier BV]
卷期号:244: 122849-122849 被引量:5
标识
DOI:10.1016/j.eswa.2023.122849
摘要

Driver distraction-level recognition while performing secondary tasks in full-touch in-vehicle information systems (FTIVISs) is essential for the harmonious co-driving of human and intelligent vehicle systems. However, there has been little research on this topic. To respond to this issue, this paper proposes a distraction-level recognition framework with a combination of semi-supervised learning, unsupervised learning, and supervised learning. First, unsupervised learning is used to set distraction-level labels. The multilayer perceptron (MLP)-AutoEncoder model and the density peaks clustering (DPC) model are introduced to divide the collected unlabeled samples of driving distraction behavior into three categories of distraction levels: high, medium, and low. Second, the factors influencing the distraction level are explored through a mixed model analysis. Finally, a stacking-based ensemble learning model is proposed to recognize the driver distraction level by supervised learning, with the influencing factors of the distraction level used as model input parameters. The proposed model has base classifiers that include random forest (RF), a gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). We conducted a real road experiment under different road and FTIVIS task conditions, and the proposed model performed better than traditional machine learning models. In addition, the model exhibited the greatest advantage when the deep neural network (DNN) algorithm was used as the meta-classifier of the model, with a recognition accuracy of 92.5%. The study findings are significant for developing a human–machine co-driving control strategy and improving vehicle driving safety.
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