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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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References

  • Borkar A, Hayes M, Smith MT (2011) A new multi-camera approach for lane departure warning. In: 13th international conference on advanced concepts for intelligent vision, Aug 22–25, vol 6915, pp 58–69

  • Chien TY, Chung SL (2014) Android-based driving assistant for lane detection and departure warning. In: 33rd Chinese control and decision conference, Nanjing, Peoples Republic of China, pp 174–179

  • Dahmani H, Chadli M, Rabhi A (2011) Vehicle dynamics and road curvature estimation for lane departure warning system using robust fuzzy observers: experimental validation. Veh Syst Dyn 53(8):1135–1149

    Article  Google Scholar 

  • Dahmani H, Chadli M, Rabhi A (2013) Road curvature estimation for vehicle lane departure detection using a robust Takagi–Sugeno fuzzy observer. Veh Syst Dyn 51(5):581–599

    Article  Google Scholar 

  • Gianni C, Alessandro C, Giuseppe F (2010) Data fusion algorithms for lane departure warning systems. In: Proceedings of the American control conference, Baltimore, pp 5344–5349

  • Huo CL, Yu Y, Syu J (2011) Vehicle warning system for land departure and collision avoidance: using fuzzy decision making. In: IEEE international conference on fuzzy systems, Taipei, Taiwan, pp 1554–1559

  • Jiang R, Klette R, Vaudrey T (2011) Lane detection and tracking using a new lane model and distance transform. Mach Vis Appl 22(4):721–737

    Article  Google Scholar 

  • Kobayashi K, Cheok KC, Watanabe K (1995) Estimation of absolute vehicle speed using fuzzy logic rule-based Kalman filter. Proc Am Control Conf 5:3086–3090

    Google Scholar 

  • Kyun Jeong H, Jeong Y (2014) FPGA implementation of AVM-based lane departure warning system. J Korean Inst Inf Technol 12(11):59–68

    Google Scholar 

  • Lei J, Yang J, Zhao J et al (2016) Backstepping sliding mode lane keeping control of lateral position error with dynamic of tire steering device. Optik Int J Light Electron Opt 127(5):2439–2443

    Article  Google Scholar 

  • Madrid N, Hurtik P (2016) Lane departure warning for mobile devices based on a fuzzy representation of images. In: 12th international conference on fuzzy set theory and applications, Liptovsky Jan, Slovakia, pp 144–159

  • Moon S, Lee S-G, Kim M (2014) Assessment and reliability validation of lane departure assistance system based on DGPS-GIS using camera vision. Trans KSAE 22(6):49–58

    Article  Google Scholar 

  • Ozcan B, Boyraz P, Yigit CB (2014) A monoSLAM approach to lane departure warning system. In: IEEE ASME international conference on advanced intelligent mechatronics, Besacon, France, pp 640–645

  • Pongtep A, Ryuta T, Toshihiro W (2011) On the use of stochastic driver behavior model in lane departure warning. IEEE Trans Intell Transp Syst 12(1):174–183

    Article  Google Scholar 

  • Salari E, Ouyang D (2013) Camera-based forward collision and lane departure warning systems using SVM. In: 56th IEEE international Midwest symposium on circuits and systems conference proceedings, Ohio Union, Columbus, pp 1278–1281

  • Sharma R, Taubel G, Yang J-S (2014) An optical flow and Hough transform based approach to a lane departure warning system. In: 11th IEEE international conference on control and automation ICCA, Taichung, Taiwan, pp 688–693

  • Tapia Espinoza R, Torres Torriti M (2013) Robust lane sensing and departure warning under shadows and occlusions. Sensors 13(3):3270–3298

    Article  Google Scholar 

  • Vijay G, Shashikant L (2015) Lane departure identification for advanced driver assistance. IEEE Trans Intell Transp Syst 16(2):910–918

    Google Scholar 

  • Wang J, Lin C, Chen S (2010) Applying fuzzy method to vision-based lane detection and departure warning system. Expert Syst Appl 37(1):113–126

    Article  Google Scholar 

  • Zhang J, Jia X, Li J (2015) Integration of scanning and image processing algorithms for lane detection based on fuzzy method. J Intell Fuzzy Syst 2015(29):2779–2786

    Article  Google Scholar 

Download references

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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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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