Research on Driving Behavior Recognition Algorithm and Implementation Based on Convolutional Neural Networks

Abstract

The technology of driver behavior recognition holds significant importance in modern traffic management and autonomous driving systems. This paper revolves around Convolutional Neural Networks (CNNs) as the cornerstone, delving into the design and implementation of driver behavior recognition algorithms. Initially, image preprocessing using OpenCV lays the foundation for subsequent neural network processing. Subsequently, critical components within the neural network, such as activation functions and loss functions, are elaborated upon, alongside discussions on gradient-based optimization methods and backpropagation algorithms. Furthermore, this study provides an in-depth analysis of the roles of convolutional layers and pooling layers within CNNs, emphasizing their applications in driver behavior recognition. Lastly, by detailing specific algorithmic workflows and network architectures, the development of a driver behavior detection system is achieved. This research supports advancements in autonomous driving and traffic safety, demonstrating the vast potential of convolutional neural networks in practical applications.

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