Abstract
Although distracted driving recognition is of great significance to traffic safety, drivers are reluctant to provide their own personalized driving data to machine learning because of privacy protection. How to improve the accuracy of distracted driving recognition on the basis of ensuring privacy protection? To address the issue, we proposed the federated shallow-CNN recognition framework (Fed-SCNN). Firstly, a hybrid model is established on the user-side through DNN and shallow-CNN, which recognizes the data of the in-vehicle images and uploads the encrypted parameters to the cloud. Secondly, the cloud server performs federated learning on major parameters through DNN to build a global cloud model. Finally, The DNN is updated in the user-side to further optimize the hybrid model. The above three steps are cycled to iterate the local hybrid model continuously. The Fed-SCNN framework is a dynamic learning process that addresses the two major issues of data isolation and privacy protection. Compared with the existing machine learning method, Fed-SCNN has great advantages in accuracy, safety, and efficiency and has important application value in the field of safe driving.
Highlights
With the rapid development of the economy, the frequency of traffic accidents is increasing year by year
According to the National Highway Traffic Safety Administration (NHTSA), nearly 30% of traffic accidents in the United States are related to driving distraction [2]
A hybrid model is established on the userside through deep neural networks (DNN) [16] and shallow-Convolutional neural network (CNN), which recognizes the data of the in-vehicle images and uploads the encrypted parameters to the cloud
Summary
With the rapid development of the economy, the frequency of traffic accidents is increasing year by year. Based on the above two challenges, the recognition of distracted driving cannot obtain the data of a large number of users in practical applications, which seriously restricts the development of this research. A federated shallow-CNN [15] recognition framework for distracted driving (Fed-SCNN) is proposed. Fed-SCNN can protect personal privacy and effectively solve the problem of data islands and have higher recognition accuracy, which has important application value in the field of safe driving, which provides a new idea for distracted driving. E framework proposed in this paper is a dynamic learning process, which continuously enhances the recognition ability of distracted driving on the basis of privacy protection and can support users to join friendly, which has better scalability
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