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Automatic identification method for driving risk status based on multi-sensor data

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Abstract

Real risk status detection is an effective way to reflect risky or dangerous driving behaviors and therefore to prevent road traffic accidents. However, a driver’s risk status is not only difficult to define but also uncontrollable and uncertain. In this study, a simulated experiment with 30 drivers was conducted using a driving simulator to collect the multi-sensor data of road conditions, humans, and vehicles. The driving risk status was classified into three states (0 - incident, 1 - near crash, or 2 - crash) on the basis of the playback system of the driving simulator. The experimental data were pre-processed using the cubic spline interpolation method and the time-windows theory. A driving risk status identification model was established using the C5.0 decision tree algorithm, and the receiver operating characteristic curve (ROC) was adopted to evaluate the performance of the identification model. The results indicated that respiration (RESP), vehicle speed (SPE), SM_FATIGUE, distance to the left lane (LLD), course angle (CA), and skin conductivity (SC) had a significant correlation (p < 0.05) with the driving risk status. The identification accuracy of the C5.0 decision tree algorithm was 78%, and the areas under the ROC were 0.934, 0.77, and 0.845, respectively. Moreover, compared with other four identification algorithms, the algorithm performance evaluation indexes TPR (0.780), precision (0.753), recall (0.78), F-measure (0.756), and kappa (0.884) of the C5.0 decision tree were all the best. The conclusion can provide reference evidence for danger warning systems and intelligent vehicle design.

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Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

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Funding

This study is sponsored by the National Natural Science Foundation of China under Grants 51805169, 52072288. This study is also supported by Natural Science Foundation of Jiangxi Province under Grant 20202BABL212009. This work is also sponsored by the National Natural Science Foundation of China under Grant 52072288. This study is also sponsored by the Special Fund for Graduate Student Innovation of Jiangxi Province (YC2020-S330).

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Authors and Affiliations

  1. School of Transportation and Logistics, East China Jiaotong University, Nanchang, China

    Lixin Yan, Yike Gong, Zhenyun Li & Junhua Guo

  2. Intelligent Transport Systems Research Center, Wuhan University of Technology, Wuhan, China

    Zhijun Chen

  3. Engineering Research Center for Transportation Safety, Ministry of Education, Wuhan, China

    Zhijun Chen

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  1. Lixin Yan
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  2. Yike Gong
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  3. Zhijun Chen
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  4. Zhenyun Li
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  5. Junhua Guo
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Correspondence to Zhijun Chen.

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Yan, L., Gong, Y., Chen, Z. et al. Automatic identification method for driving risk status based on multi-sensor data. Pers Ubiquit Comput 27, 1303–1319 (2023). https://doi.org/10.1007/s00779-021-01580-x

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  • DOI: https://doi.org/10.1007/s00779-021-01580-x

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