Transactions on Machine Intelligence

Transactions on Machine Intelligence

Intelligent Transportation in The Prevention of Accidents

Document Type : Original Article

Authors
S. Asadzadeh 1
O. Rahmani Seryasat 2
S. Moshrefzadeh 3
1 Department of Computer Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran
2 Assistant Prof, Department of Electrical Engineering, Shams Higher Education Institute, Gorgan, Iran
3 Department of Computer Engineering, Faculty of Technology and Engineering, Yasouj University, Yasouj, Iran
10.47176/TMI.2018.49
Abstract
Information technology has significantly influenced numerous industrial sectors, and its integration into transportation systems has emerged as a promising solution, giving rise to intelligent transportation systems (ITS). Among the key application areas of ITS is the use of computer vision for accident prevention. Specifically, intelligent driver monitoring systems play a crucial role in enhancing vehicle safety by proactively identifying and addressing conditions that may lead to accidents. These systems aim to assist and alert drivers by intelligently recognizing potentially dangerous situations, thereby contributing to a substantial reduction in traffic accidents and related incidents. A major concern within intelligent transportation is driver drowsiness, a critical factor in preventing severe financial and human losses caused by traffic accidents. To address this, intelligent systems are employed to enhance vehicle control by continuously analyzing the driver’s physical state and behavioral patterns. Consequently, the development of a system capable of accurately assessing a driver’s alertness or fatigue level based on both driver behavior and vehicle status holds great importance. Notably, the proposed system presented in this research demonstrates superior performance compared to existing approaches, achieving 96% accuracy, 94% sensitivity, and 94% specificity in detecting driver drowsiness.
Keywords
Intelligent Transportation
Computer Vision
Accidents
Smart City
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Transactions on Machine Intelligence
Volume 1, Issue 1
Winter 2018
Pages 49-56

  • Receive Date 16 January 2022
  • Revise Date 24 February 2022
  • Accept Date 20 March 2022