location：Home > 2021 Vol.4 Mar.No.1 > Self-adaptability of collision warning for motor vehicle drivers

2021 Vol.4 Mar.No.1

  • Title: Self-adaptability of collision warning for motor vehicle drivers
  • Name: Mohamed Baza
  • Company: Department of Computer Science, Sam Houston State University, Huntsville, TX 77340, USA
  • Abstract:

    In order to improve the self-adaptability and tracking ability of vehicle driver collision warning, analyze the quantitative characteristics of awareness recognition for warning accuracy, and propose an adaptive control method for vehicle driver collision warning based on machine vision and fuzzy control. Construct the intelligent control model of the motor vehicle driver collision warning device, adopt the machine vision feature analysis method, establish the driver collision warning positioning analysis model, and realize the adaptive fusion parameter analysis of the motor vehicle driver collision warning through fuzzy adaptive feature analysis , In the visual distribution space of vehicle driver collision warning, realize the optimization of the quantitative parameters of the vehicle driver collision warning, optimize the analysis of the control constraint factors of the vehicle driver collision warning, and combine the optimized control law and parameter identification The method realizes the identification of target parameters for the collision warning of motor vehicle drivers. The simulation results show that the quantitative analysis ability of this method for vehicle driver collision warning is better, and awareness recognition is significant for improving the warning accuracy.

     

  • Keyword: motor vehicle; driver; collision warning; adaptive
  • DOI: 10.12250/jpciams2021090117
  • Citation form: Mohamed Baza.Self-adaptability of collision warning for motor vehicle drivers[J]. Computer Informatization and Mechanical System,2021,Vol.4,pp.1-6
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Tsuruta Institute of Medical Information Technology
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