Design of a dangerous driving prevention and control system based on deep learning

Zheng Yang; Zhangxiang Jing; Jingfeng Yang; Qiaozhi Li; Feng Zeng; Jiacheng Yan; Huayang Cao; Rongcan Li

doi:10.54254/2977-3903/2025.29839

Article Information

Received: 21 October 2025

Published: 20 November 2025

DOI: https://doi.org/10.54254/2977-3903/2025.29839

Article Type: Research Article

Cite this article

Yang,Z.;Jing,Z.;Yang,J.;Li,Q.;Zeng,F.;Yan,J.;Cao,H.;Li,R. (2025). Design of a dangerous driving prevention and control system based on deep learning. Advances in Engineering Innovation,16(10),189-200.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About this Journal

Journal: Advances in Engineering Innovation

Volume: Vol.16, Issue 10

ISSN: 2977-3903 (Print) / 2977-3911 (Online)

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[6]Xiang, H., Zhu, J., Liang, G., et al. (2021). Prediction of Dangerous Driving Behavior Based on Vehicle Motion State and Passenger Feeling Using Cloud Model and Elman Neural Network.Frontiers in Neurorobotics, 15.

[7]Huang, W., Liu, X., Luo, M., et al. (2019). Video-based Abnormal Driving Behavior Detection via Deep Learning Fusions.IEEE Access, 7, 1-1.

[8]Chen, C., Seff, A., Kornhauser, A., & Xiao, J. (2015). DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving. ICCV, 2722-2730.Scanlon, J. M., Kusano, K. D., & Sherony, R. (2015). Potential safety benefits of lane departure warning and prevention systems in the U.S. vehicle fleet.Physica C: Superconductivity, 210(s3-4), 401-407.

[9]Cicchino, J. B. (2017). Effects of blind spot monitoring systems on police-reported lane-change crashes (Doctoral dissertation). Insurance Institute for Highway Safety.

[10]Koesdwiady, A., Bedawi, S. M., Ou, C., & Karray, F. (2017). End-to-end deep learning for driver distraction recognition.\textit{Image Analysis and Recognition}, 11-18.

[11]Kenney, J. B. (2011). Dedicated short-range communications (DSRC) standards in the United States.Proceedings of the IEEE, 99(7), 1162-1182.

[12]Bojarski, M, etal.(2016). End to End Learning for Self-Driving Cars. \textit{arXiv preprint} arXiv: 1604.07316.

[13]Xiang, H., Zhu, J., Liang, G., et al. (2021). Prediction of Dangerous Driving Behavior Based on Vehicle Motion State and Passenger Feeling Using Cloud Model and Elman Neural Network.Frontiers in Neurorobotics, 15.

[14]Huang, W., Liu, X., Luo, M., et al. (2019). Video-based Abnormal Driving Behavior Detection via Deep Learning Fusions.IEEE Access, 7, 1-1.

[15]Chen, C., Seff, A., Kornhauser, A., & Xiao, J. (2015). DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving. ICCV, 2722-2730.

[16]Scanlon, J. M., Kusano, K. D., & Sherony, R. (2015). Potential safety benefits of lane departure warning and prevention systems in the U.S. vehicle fleet.Physica C: Superconductivity, 210(s3-4), 401-407.

[17]Cicchino, J. B. (2017). Effects of blind spot monitoring systems on police-reported lane-change crashes (Doctoral dissertation). Insurance Institute for Highway Safety.

[18]Koesdwiady, A., Bedawi, S. M., Ou, C., & Karray, F. (2017). End-to-end deep learning for driver distraction recognition.\textit{Image Analysis and Recognition}, 11-18.

[19]Kenney, J. B. (2011). Dedicated short-range communications (DSRC) standards in the United States.Proceedings of the IEEE, 99(7), 1162-1182.

[20]Bojarski, M, etal.(2016). End to End Learning for Self-Driving Cars. \textit{arXiv preprint} arXiv: 1604.07316.

[21]Xiang, H., Zhu, J., Liang, G., et al. (2021). Prediction of Dangerous Driving Behavior Based on Vehicle Motion State and Passenger Feeling Using Cloud Model and Elman Neural Network.Frontiers in Neurorobotics, 15.

[22]Huang, W., Liu, X., Luo, M., et al. (2019). Video-based Abnormal Driving Behavior Detection via Deep Learning Fusions.IEEE Access, 7, 1-1.

[23]Chen, C., Seff, A., Kornhauser, A., & Xiao, J. (2015). DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving. ICCV, 2722-2730.

[24]Scanlon, J. M., Kusano, K. D., & Sherony, R. (2015). Potential safety benefits of lane departure warning and prevention systems in the U.S. vehicle fleet.Physica C: Superconductivity, 210(s3-4), 401-407.

[25]Cicchino, J. B. (2017). Effects of blind spot monitoring systems on police-reported lane-change crashes (Doctoral dissertation). Insurance Institute for Highway Safety.

[26]Koesdwiady, A., Bedawi, S. M., Ou, C., & Karray, F. (2017). End-to-end deep learning for driver distraction recognition.\textit{Image Analysis and Recognition}, 11-18.

[27]Kenney, J. B. (2011). Dedicated short-range communications (DSRC) standards in the United States.Proceedings of the IEEE, 99(7), 1162-1182.

[28]Bojarski, M, etal.(2016). End to End Learning for Self-Driving Cars. \textit{arXiv preprint} arXiv: 1604.07316.

[29]Xiang, H., Zhu, J., Liang, G., et al. (2021). Prediction of Dangerous Driving Behavior Based on Vehicle Motion State and Passenger Feeling Using Cloud Model and Elman Neural Network.Frontiers in Neurorobotics, 15.

[30]Huang, W., Liu, X., Luo, M., et al. (2019). Video-based Abnormal Driving Behavior Detection via Deep Learning Fusions.IEEE Access, 7, 1-1.