An abnormal driving behavior recognition algorithm based on the temporal convolutional network and soft thresholding. Issue 9 (8th February 2022)

• Record Type: Journal Article
• Authors: Zhao, Yunyun; Jia, Hongwei; Luo, Haiyong; Zhao, Fang; Qin, Yanjun; Wang, Yueyue
• Is Part Of: International journal of intelligent systems. Volume 37:Issue 9(2022)
• Page Start: 6244
• Page End: 6261
• Publication Date: 2022-02-08
• Subjects: abnormal driving behaviors -- deep learning -- soft attention -- soft thresholding -- TCN; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22842
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: Most traffic accidents are caused by bad driving habits. Online monitoring of the abnormal driving behaviors of drivers can help reduce traffic accidents. Recently, abnormal driving behavior recognition based on the sensors' data embedded in commodity smartphones has attracted much attention. Though much progress has been made about driving behavior recognition, the existing works cannot achieve high recognition accuracy and show poor robustness. To improve the driving behaviors recognition accuracy and robustness, we propose an algorithm based on S oft Thresholding and T emporal C onvolutional N etwork (S‐TCN) for driving behavior recognition. In this algorithm, we first introduce a soft attention mechanism to learn the importance of different sensors. The TCN has the advantages of small memory requirement and high computational efficiency. And the soft thresholding can further filter the redundant features and extract the main features. So, we fuse the TCN and soft thresholding to improve the model's stability and accuracy. Our proposed model is extensively evaluated on four real public data sets. The experimental results show that our proposed model outperforms best state‐of‐the‐art baselines by 2.24 %.