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. 2022 Jan 25;17(1):e0263030.
doi: 10.1371/journal.pone.0263030. eCollection 2022.

A hybrid neural network for driving behavior risk prediction based on distracted driving behavior data

Xin Fu  1 , Hongwei Meng  1 , Xue Wang  1 , Hao Yang  2 , Jianwei Wang  3
Affiliations

A hybrid neural network for driving behavior risk prediction based on distracted driving behavior data

Xin Fu et al. PLoS One. .

Abstract

Distracted driving behavior is one of the main factors of road accidents. Accurately predicting the risk of driving behavior is of great significance to the active safety of road transportation. The large amount of information collected by the sensors installed on the vehicle can be identified by the algorithm to obtain the distracted driving behavior data, which can be used to predict the driving behavior risk of the vehicle and the area. In this paper, a new neural network named Driving Behavior Risk Prediction Neural Network (DBRPNN) is developed for prediction based on the distracted driving behavior data. The network consists of three modules: the Feature Processing Module, the Memory Module, and the Prediction Module. In this process, attribute data (time in a day, daily driving time, and daily driving mileage) that can reflect external factors and driver statuses, are added to the network to increase the accuracy of the model. We predicted the driving behavior risk of different objects (Vehicle and Area). For the applicability improvement of the model, we further classify the distracted driving behavior categories, and DBRPNN can provide more accurate risk prediction. The results show that compared with traditional models (Classification and Regression Tree, Support Vector Machines, Recurrent Neural Network, and Long Short-Term Memory), DBRPNN has better prediction performance. The method proposed in this paper has been fully verified and may be transplanted into active safety early warning system for more accurate and flexible application.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Driving behavior risk prediction neural-network (DBRPNN) architecture.
Fig 2
Fig 2. LSTM network architecture.
Fig 3
Fig 3. Distracted driving behavior coordinate point.
The coordinates of vehicles at the time of distracted driving behavior can be seen in the figure, which is mainly distributed in Shaanxi Province and some surrounding provinces.
Fig 4
Fig 4. Scale of each distracted driving behavior code.
Fig 5
Fig 5. Daily average number of distracted driving behaviors.
Fig 6
Fig 6. The prediction result of the risk level.
(a) Vehicle ID 6096. (b) Vehicle ID 21973. (c) Area ID (53,24). (d) Area ID (53,23). (e) Category 104 of Vehicle ID 20635.
See this image and copyright information in PMC

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