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Research and design of illegal driving behavior detection model based on deep learning

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DOI: 10.23977/acss.2024.080707 | Downloads: 4 | Views: 112

Author(s)

Bingqing Chen 1, Yan Zhou 1, Hailing Zhang 1, Shuhan Zuo 1

Affiliation(s)

1 Anhui University of Finance and Economics, Bengbu, 233030, China

Corresponding Author

Bingqing Chen

ABSTRACT

The rapid development of transportation systems and the growing number of vehicles on roads have significantly increased traffic-related risks, especially due to illegal driving behaviors such as speeding, distracted driving, and unauthorized lane changes. These behaviors not only disrupt traffic flow but also contribute to severe accidents, property damage, and fatalities. Traditional traffic monitoring techniques, such as radar-based systems and manual surveillance, are inadequate to address these complex challenges due to their dependency on predefined rules and limited scalability. This research introduces a robust illegal driving behavior detection model built on the principles of deep learning. By combining convolutional neural networks (CNNs) for spatial feature extraction and long short-term memory (LSTM) networks for temporal analysis, the proposed model captures complex driving patterns from traffic video data. A large-scale dataset featuring diverse driving scenarios and behaviors was used to train and validate the model, achieving a remarkable accuracy of 95%. The study not only demonstrates the potential of deep learning in traffic law enforcement but also highlights its advantages in scalability, automation, and real-time decision-making. This paper provides valuable insights for researchers and policymakers aiming to implement intelligent traffic management systems.

KEYWORDS

Illegal driving behavior, deep learning, traffic monitoring, intelligent transportation, road safety

CITE THIS PAPER

Bingqing Chen, Yan Zhou, Hailing Zhang, Shuhan Zuo, Research and design of illegal driving behavior detection model based on deep learning. Advances in Computer, Signals and Systems (2024) Vol. 8: 57-62. DOI: http://dx.doi.org/10.23977/acss.2024.080707.

REFERENCES

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