Driver distraction detection method based on fusion attention mechanism and Bi-LSTM

By introducing a method of fusion attention mechanism and Bi-LSTM structure in driver distraction detection, the limitations of large-scale context information processing in the prior art and the difficulty of adapting to different scenarios are solved, and higher detection accuracy and stability are achieved.

CN119580232APending Publication Date: 2025-03-07GUANGDONG UNIV OF TECH
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Patent Information

Application Number
CN202411727008.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-28
Publication Date
2025-03-07

AI Technical Summary

Technical Problem

The existing driver distraction detection methods have limitations in processing large-scale context information, which are difficult to adapt to different driving environments and scenarios, and cannot fully capture the temporal evolution characteristics of distraction behavior.

Method used

The driver distraction detection method based on the fusion attention mechanism and Bi-LSTM is adopted, and the time series data is processed through the Bi-LSTM structure, and the global context information is captured in combination with the attention mechanism to enhance the receptive field and generalization ability of the model.

Benefits of technology

It significantly improves the accuracy and stability of driver distraction detection, enhances the model's adaptability to different driving environments and scenarios, and can more accurately identify subtle changes in driver behavior and temporal evolution characteristics.

✦ Generated by Eureka AI based on patent content.

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Abstract

The invention discloses a driver distraction detection method based on a fusion attention mechanism and Bi-LSTM, and the method comprises the steps: collecting an original image of a driver, carrying out the data preprocessing of the original image of the driver, and obtaining a target data set; constructing a driver distraction detection network model based on a fusion attention mechanism and Bi-LSTM, and inputting the target data set into the driver distraction detection network model for model training to obtain a trained model; evaluating the overall performance of the trained model according to the performance index, and obtaining a target detection model with the driver distraction detection capability; and the target detection model is deployed and migrated to an actual vehicle-mounted environment of the new energy vehicle, driver behaviors are detected in real time, and a real-time detection feedback result is obtained. According to the invention, the accuracy of distraction behavior detection is improved.
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Citation Information

Patent Citations

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