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Lane departure warning algorithm based on probability statistics of driving habits

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Abstract

For the different degrees of danger caused by different driving habits, a lane departure warning algorithm based on probability statistics of driving habits is proposed in this paper. According to the different driving habits of different drivers, the early warning mechanism can be adaptively adjusted through the method of probability statistics to make lane departure warning more targeted and accurate. Firstly, each frame of image is preprocessed, including gray treatment, edge detection and binarization. Then, Canny operator is used to detect the edge, and Hough transform is applied to detect the lines. And the lane median line equation for the detection and identification of lane also can be calculated. After that, the image coordinate system is transformed into the world coordinate system by means of the formula and matrix of coordinate conversion. According to the theory of Kalman filter, the statistics of lateral acceleration and lateral velocity are updated continuously, and the position of the vehicle in the next moment is predicted by the state transition equation and the forecast equation. From the results of experiments and the comparison with exhaustive algorithms, the advantages of using Kalman filter to predict the location of vehicles and the improved time-to-lane-crossing combined with probabilistic statistics to warning are illustrated clearly.

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Funding

This study was funded by the National Key Research and Development Program of China (2017YFB0102500, 2017YFB0102600), Natural Science Foundation of Jilin Province (20170101133JC), the Korea Foundation for Advanced Studies’ International Scholar Exchange Fellowship for the academic year of 2017–2018 and Jilin University (5157050847, 2017XYB252).

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchun, 130012, China

    Jindong Zhang, Jiaxin Si & Xuelong Yin

  2. Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Jilin University, Changchun, 130012, China

    Jindong Zhang

  3. State Key Laboratory of Automobile Simulation and Control, Jilin University, Changchun, 130025, China

    Jindong Zhang & Zhenhai Gao

  4. Division of Computer Science, College of Computing, Hanyang University, 1271 Sa-3 Dong, Ansan, 426-791, Republic of Korea

    Young Shik Moon

  5. China Automotive Technology and Research Center, Tianjin, 300300, China

    Jinfeng Gong

  6. College of Mechanical Engineering, Hebei University of Technology, Tianjin, 300132, China

    Fengmin Tang

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  1. Jindong Zhang
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  2. Jiaxin Si
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  3. Xuelong Yin
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  4. Zhenhai Gao
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  5. Young Shik Moon
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  6. Jinfeng Gong
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  7. Fengmin Tang
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Correspondence to Jindong Zhang.

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Conflict of interest

Jindong Zhang declares that he has no conflict of interest. Jiaxin Si declares that he has no conflict of interest. Xuelong Yin declares that he has no conflict of interest. Zhenhai Gao declares that he has no conflict of interest. Young Shik Moon declares that he has no conflict of interest. Jinfeng Gong declares that he has no conflict of interest. Fengmin Tang declares that he has no conflict of interest.

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Zhang, J., Si, J., Yin, X. et al. Lane departure warning algorithm based on probability statistics of driving habits. Soft Comput 25, 13941–13948 (2021). https://doi.org/10.1007/s00500-020-04704-2

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  • DOI: https://doi.org/10.1007/s00500-020-04704-2

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