Abstract

Drinking-driving behaviors are important causes of road traffic injuries, which are serious threats to the lives and property of traffic participants. Therefore, reducing the occurrences of drinking-driving behaviors has become an important problem of traffic safety research. Forty-eight male drivers and six female drivers who could drink moderate alcohol were chosen as participants. The drivers’ physiological data, operation behavior data, car running data, and driving environment data were collected by designing various virtual traffic scenes and organizing drivers to conduct driving simulation experiments. The original variables were analyzed by the Principal Component Analysis (PCA), and seven principal components were extracted as the input vector of the Radial Basis Function (RBF) neural network. The principal component data was used to train and verify the RBF neural network. The Levenberg-Marquardt (LM) algorithm was chosen to train the parameters of the neural network and build a drinking-driving recognition model based on PCA and RBF neural network to realize an accurate recognition of drinking-driving behaviors. The test results showed that the drinking-driving recognition model based on PCA and RBF neural network could identify drinking drivers accurately during driving process with a recognition accuracy of 92.01%, and the operation efficiency of the model was high. The research can provide useful reference for prevention and treatment of drinking and driving and traffic safety maintenance.

Highlights

  • With the development of the automobile industry, the world’s car ownership has risen sharply

  • The principal components were the characteristic parameters of drinking-driving behaviors, which were used as the input vector to train the Radial Basis Function (RBF) neural network and build the drinking-driving recognition model

  • Principal Component Analysis (PCA), and a few characteristic parameters containing the most of the driving information were extracted by PCA

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Summary

Introduction

With the development of the automobile industry, the world’s car ownership has risen sharply. Cars are convenient for people's livelihood, but they bring serious traffic safety problems [1]. Traffic accidents accounted for 61.54%, and the death toll percentage of traffic accidents was 58.61%, which was the highest death toll percentage among the various accidents [2]. Drinking and driving is an important factor which increases traffic accident death rates related to young adults [3]. It has been a key issue in the traffic safety field to take effective measures to reduce or eliminate drinking and driving

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