We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
An abnormal driving behavior recognition algorithm based on the temporal convolutional network and soft thresholding.
- Authors
Yunyun Zhao; Hongwei Jia; Haiyong Luo; Fang Zhao; Yanjun Qin; Yueyue Wang
- 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 Soft Thresholding and Temporal Convolutional Network (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-theart baselines by 2.24%.
- Subjects
CONVOLUTIONAL neural networks; MOTOR vehicle driving; SOFT sets; TRAFFIC accidents; ALGORITHMS
- Publication
International Journal of Intelligent Systems, 2022, Vol 37, Issue 9, p6244
- ISSN
0884-8173
- Publication type
Article
- DOI
10.1002/int.22842