Driver’s mobile phone usage detection using guided learning based on attention features and prior knowledge

Huang, Tao; Fu, Rui; Sun, Qinyu

doi:10.1016/j.eswa.2022.117877

Cited by 6 publications

(2 citation statements)

References 28 publications

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“…Considering that distraction behavior recognition is a finegrained image classification task, to improve the ability of the model to extract subtle features from images with small differences, Li, et al [16] guided the model to learn robust features based on the loss function of the contrast learning and stop-gradient strategies. In addition, the improvement of the classification performance of deep learning models for distraction behaviors by applying attention mechanisms and prior knowledge is also an important research idea [17][18][19][20]. Lu, et al [18] applied the attention channel to convolutional weights, and fused global and keypoint features from driving images of different scales.…”

Section: B Deep Learning Feature Extraction and Classificationmentioning

confidence: 99%

Driver Distraction Behavior Detection Framework Based on the DWPose Model, Kalman Filtering, and Multi-Transformer

Shi

2024

IEEE Access

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Section: B Deep Learning Feature Extraction and Classificationmentioning

confidence: 99%

Driver Distraction Behavior Detection Framework Based on the DWPose Model, Kalman Filtering, and Multi-Transformer

Shi

2024

IEEE Access

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“…That is, transfer learning uses the source task as the basis for the target task. In the pre-training process, the target task and the source task are required to be interrelated; that is, the source task belongs to the classification task, and the sample size of the dataset used by the source task must be large enough and the quality good enough [ 39 ].…”

Section: Our Proposed Mdpmeb Algorithmmentioning

confidence: 99%

Electric Bus Pedal Misapplication Detection Based on Phase Space Reconstruction Method

Lyu,

Li,

Zhang

et al. 2023

Sensors

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Due to the environmental protection of electric buses, they are gradually replacing traditional fuel buses. Several previous studies have found that accidents related to electric vehicles are linked to Unintended Acceleration (UA), which is mostly caused by the driver pressing the wrong pedal. Therefore, this study proposed a Model for Detecting Pedal Misapplication in Electric Buses (MDPMEB). In this work, natural driving experiments for urban electric buses and pedal misapplication simulation experiments were carried out in a closed field; furthermore, a phase space reconstruction method was introduced, based on chaos theory, to map sequence data to a high-dimensional space in order to produce normal braking and pedal misapplication image datasets. Based on these findings, a modified Swin Transformer network was built. To prevent the model from overfitting when considering small sample data and to improve the generalization ability of the model, it was pre-trained using a publicly available dataset; moreover, the weights of the prior knowledge model were loaded into the model for training. The proposed model was also compared to machine learning and Convolutional Neural Networks (CNN) algorithms. This study showed that this model was able to detect normal braking and pedal misapplication behavior accurately and quickly, and the accuracy rate on the test dataset is 97.58%, which is 9.17% and 4.5% higher than the machine learning algorithm and CNN algorithm, respectively.

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Passenger non-driving related tasks detection using a light weight neural network based on human prior knowledge and soft-hard feature constraints

Guo

et al. 2023

Expert Systems with Applications

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Driver’s mobile phone usage detection using guided learning based on attention features and prior knowledge

Cited by 6 publications

References 28 publications

Driver Distraction Behavior Detection Framework Based on the DWPose Model, Kalman Filtering, and Multi-Transformer

Driver Distraction Behavior Detection Framework Based on the DWPose Model, Kalman Filtering, and Multi-Transformer

Electric Bus Pedal Misapplication Detection Based on Phase Space Reconstruction Method

Passenger non-driving related tasks detection using a light weight neural network based on human prior knowledge and soft-hard feature constraints

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