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Intelligent Driving Behavior Recognition and Legal Liability Issues Using Deep Learning Convolutional Networks

Intelligent Driving Behavior Recognition and Legal Liability Issues Using Deep Learning Convolutional Networks

Jiaxuan Wei (College of Intellectual Property, Hubei University of Automotive Technology, China), Shuang Yu (Social Law Department, Gyeongsang National University, South Korea), and Yu Wang (Law Department, Gyeongsang National University, South Korea)
Copyright: © 2025 |Pages: 20
DOI: 10.4018/IJITSA.382479
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Abstract

This work aimed to develop a driving behavior recognition and liability assistance determination method applicable to practical traffic safety management and criminal liability determination scenarios. First, an improved deep neural network was designed, which integrated multi-scale 3D convolutional structures and attention mechanisms to efficiently extract driving behavior features from both spatial and temporal dimensions. Next, a subset of six typical driving behaviors was constructed based on the Drive&Act public dataset, followed by sample labeling and feature preprocessing. Finally, based on behavior recognition outputs, a legal article logic mapping model and a behavior risk scoring mechanism were proposed to quantify the legal liability risks corresponding to driving behaviors.
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Research Objectives

This work focused on the core issues of driving behavior recognition and criminal liability inference and proposed a DL model with task adaptability and legal mapping capabilities. Moreover, it constructed corresponding experimental verification systems and liability inference mechanisms. The specific research objectives included designing and implementing a driving behavior recognition model based on an improved multi-scale 3D convolutional neural network (MS-3D CNN). This model targeted six typical driving behaviors for high-precision recognition. The model structure integrated multi-scale convolutional kernels, channel attention mechanisms, and temporal differential units to enhance its ability to perceive subtle behavioral features and dynamic changes.

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