Volume 40 Issue 2
Apr.  2022
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Article Navigation >  Journal of Transport Information and Safety  > 2022  >  40(2): 63-72
LI Hao, WANG Xiaoyuan, HAN Junyan, LIU Shijie, CHEN Longfei, SHI Huili. A Method for Identifying Temperament Propensity of Drivers Based on AutoNavi Navigation Data and a FOA-GRNN Model[J]. Journal of Transport Information and Safety, 2022, 40(2): 63-72. doi: 10.3963/j.jssn.1674-4861.2022.02.008
Citation: LI Hao, WANG Xiaoyuan, HAN Junyan, LIU Shijie, CHEN Longfei, SHI Huili. A Method for Identifying Temperament Propensity of Drivers Based on AutoNavi Navigation Data and a FOA-GRNN Model[J]. Journal of Transport Information and Safety, 2022, 40(2): 63-72. doi: 10.3963/j.jssn.1674-4861.2022.02.008
shu

A Method for Identifying Temperament Propensity of Drivers Based on AutoNavi Navigation Data and a FOA-GRNN Model

doi: 10.3963/j.jssn.1674-4861.2022.02.008
  • 1.

    College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, Shandong, China

  • 2.

    Collaborative Innovation Center for Intelligent Green Manufacturing Technology and Equipment of Shandong Province, Qingdao University of Science and Technology, Qingdao 266000, Shandong, China

  • Received Date: 2021-06-23
    Available Online: 2022-05-18
    Abstract
  • In order to improve the capacity of automobiles in active safety, a method for identifying driving propensity with a low-cost and high accuracy based on AutoNavi navigation data is proposed. An application to collectdriving data is developed based on Amap software development tool, which is further integrated into an intelligentterminal for data collection, procession, and storage in real time. Driver behavior data inferred from the time, speed, and acceleration of vehicles controlled by drivers with different temperament propensity are obtained through physiological, psychological and driving experiments. The principal component analysis(PCA)technique is used to extract the important factors for studying the temperament propensity of drivers, and the drivers are grouped into threedriving propensities: radical, common and the conservative. A Fruit-fly optimization algorithm(FOA)and a generalized regression neural network(GRNN)are integrated to establish a high-precision model for driving propensity identification, which is further trained and verified using collected data. The verification results show that: the overall accuracy of the identification model is 94.17%, and the identification precision of the radical, common and theconservative types are 95.06%, 92.5% and 94.93%, respectively; compared to the simple GRNN model, the overallprecision of the proposed model is improved by 5%~10%; and compared to the previous method based on inertialsensor data and the integrated method of discrete wavelet transformation and adaptive neuro fuzzy inference system, the FOA-GRNN model is more practical, and its overall precision is improved by 2.17%.

     

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