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Abstract

The contribution presents a novel approach to the detection and tracking of lanes based on lidar data. Therefore, we use the distance and reflectivity data coming from a one-dimensional sensor. After having detected the lane through a temporal fusion algorithm, we register the lidar data in a world-fixed coordinate system. To this end, we also incorporate the data coming from an inertial measurement unit and a differential global positioning system. After that stage, an original image of the road can be inferred. Based on this data view, we are able to track the lane either with a Kalman filter or by using a polynomial approximation for the underlying lane model.

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Authors and Affiliations

Michael Thuy
Fernando León
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Abstract

Lane detection is one of the key steps for developing driver assistance and vehicle automation features. A number of techniques are available for lane detection as part of computer vision tools to perform lane detection with different levels of accuracies. In this paper a unique method has been proposed for lane detection based on dynamic origin (DOT). This method provides better flexibility to adjust the outcome as per the specific needs of the intended application compared to other techniques. As the method offers better degree of control during the lane detection process, it can be adapted to detect lanes in varied situations like poor lighting or low quality road markings. Moreover, the Piecewise Linear Stretching Function (PLSF) has also been incorporated into the proposed method to improve the contrast of the input image source. Adding the PLSF method to the proposed lane detection technique, has significantly improved the accuracy of lane detection when compared to hough transform method from 87.88% to 98.25% in day light situations and from 94.15% to 97% in low light situations.
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Bibliography

[1] V. Gaikwad, S. Lokhande, “Lane departure identification for advanced driver assistance,” IEEE Transactions on Intelligent Transportation Systems., 2015, 16(2): 910–918.
[2] Sandipann P. Narote, Pradnya N. Bhujbal, Abbhilasha S. Narote, Dhiraj M. Dhane, “A review of recent advances in lane detection and departure warning,” System. Pattern Recognition, 2018, 73:216-234.
[3] P.C. Wu, C. Chang, C.H. Lin, “Lane mark extraction for automobiles under complex conditions,” Pattern Recognition, 2014, 47: 2756–2767.
[4] C. Mu, X. Ma, “Lane detection based on object segmentation and piecewise fitting,” Telkomnika Indonesian Journal of Electrical Engineering, 2014, 12(5):3491–3500.
[5] CALTECH database http://www.vision.caltech.edu/archive.html
[6] Y. Dong, J. Xiong, L. Li, J. Yang “Lane detection based on object segmentation and piecewise fitting,” ICCP proceedings, 2012, 461–464.
[7] P. Hsiao, C.W. Yeh, S. Huang, L.C. Fu, “Portable vision based real time lane departure warning system day and night,” IEEE Transactions on Vehicular Technology,2009, 58(4):2089–2094.
[8] Prashanth Viswanath, Pramod Swami, “A Robust and Real -Time Image Based Lane Departure Warning System,” IEEE International Conference on Consumer Electronics, 2016.
[9] Minghua Niu, Jianmin Zhang, Gen Li, “Research on the Algorithms of Lane Recognition based on Machine Vision,” International Journal of Intelligent Engineering and Systems, 2015, 8(4).
[10] Gulivindala Suresh,Chanamallu Srinivasa Rao, “Localization of Copy-Move Forgery in Digital Images through Differential Excitation Texture Features,” International Journal of Intelligent Engineering and Systems, 2019, 12(2).
[11] C.R. Jung, C.R. Kelber, “Lane following and lane departure using a linear parabolic mode,” Image and Vision Computing, 2005, 23(13):1192–1202.
[12] D. Kragic, L. Petersson and H.I. Christensen, “Visually guided manipulation tasks,” Robotics and Autonomous Systems, 2002, 40(2/3):193-203.
[13] J.W. Lee, “A machine vision system for lane departure detection. Computing,” Vision Image Understanding, 2002, 86(1): 52–78.
[14] J. Melo, A. Naftel, A. Bernardino, J. Santos, “Detection and classification of highway lanes using vehicle motion trajectories,” IEEE Transactions on Intelligent Transportation Systems, 2006, 7(2): 188–200.
[15] Chaiwat Nuthong; Theekapun Charoenpong, “Lane detection using smoothing,” 3rd International Congress on Image and Signal Processing, 2010, 989-993.
[16] Bing Yu; Weigong Zhang; Yingfeng Cai, “A Lane Departure Warning System Based on Machine Vision,” Proceeding IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, 2008, 197-201.
[17] J.G. Wang, C. Lin, S. Chen, “Applying fuzzy method to vision-based lane detection and departure warning system,” Expert Systems with Applications, 2010, 3(1):113–126.
[18] H. Xu, H. Li, “Study on a robust approach of lane departure warning algorithm,” IEEE International Conference on Signal Processing System (ICSPS), 2010, 201–204.
[19] S. Srivastava, M. Lumb, R. Singal, “Improved Lane Detection using Hybrid Median Filter and Modified Hough Transform,” International Journal of Advanced Research in Computer Science and Software Engineering, 2014, 4(1): 30–37.
[20] H. Aung, M.H. Zaw, “Video based lane departure warning system using hough transform,” International Conference on Advances in Engineering and Technology (ICAET), 2010, 85–88.
[21] X. An, E. Shang, J. Song, J. Li, H. He, “Real-time lane departure warning system based on a single fpga,” Eurasip Journal on Image and Video Processing, 2013,38(1–18).
[22] J. Son, H. Yoo, S. Kim, K. Sohn, “Real-time illumination invariant lane detection for lane departure warning system,” Expert Systems with Applications, 2015, 42(4):1816–1824.
[23] Y. Wang, D. Shen, E.K. Teoh, “Lane detection using spline model,” Pattern Recognition, 2000, 21(9): 677–689.
[24] C.J. Lin, J.G. Wang, S.M. Chen, C.Y. Lee, “Design of a lane detection and departure warning system using functional link-based neuro-fuzzy network,” IEEE International Conference on Fuzzy System (FUZZ), 2010, 1–7.
[25] Q. Lin, Y. Han, H. Hahn, “Real time lane detection based on extended edge-linking algorithm,” IEEE International Conference on Computer Research and Development, 2010, 725–730.
[26] C. Tu, B.V. Wyk, Y. Hamam, K. Djouni, S. Du, “Vehicle Position Monitoring using,” Hough Transform.IERI Procedia, 2013;4:316–322.
[27] E. Salari, D. Ouyang, “Camera-based forward collision and lane departure warning system using svm,” IEEE 56th International Midwest Symp. On Circuits and Systems (MWSCAS), 2013,1278–1281.
[28] A.S. Aguadoa, Eugenia, Montie and M. S. Nixonc, “Invariant characterisation of the Hough transform for pose estimation of arbitrary shapes,” Pattern Recognition, 2002, 35(5):1083-1097.
[29] Borkar, M. Hayes, M. Smith, “Robust lane detection and tracking with ransac and kalman filter,” 16th IEEE International Conference on Image Processing (ICIP), 2009, 3261–3264.
[30] N. Madrid, P. Hurtik “Lane departure warning for mobile devices based on a fuzzy representation of images,” Fuzzy Sets System, 2016, 291:144–159.
[31] P.Maya, C.Tharini, “Performance Analysis of Lane Detection Algorithm using Partial Hough Transform,” 21st International Arab Conference on Information Technology (ACIT'2020), 2020, Egypt.
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Authors and Affiliations

P. Maya
1
C. Tharini
2

  1. B S Abdur Rahman Crescent Institute of Science and Technology, Chennai, India
  2. B S Abdur Rahman Crescent Institute of Science and Technology,Chennai, India
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Abstract

Driver assistance systems have started becoming a key differentiator in automotive space and all major automotive manufacturers have such systems with various capabilities and stages of implementation. The main building blocks of such systems are similar in nature and one of the major building blocks is road lane detection. Even though lane detection technology has been around for decades, it is still an ongoing area of research and there are still several improvements and optimizations that are possible. This paper offers an Optimized Dynamic Origin Technique (Optimized DOT) for lane detection. The proposed optimization algorithm of optimized DOT gives better results in performance and accuracy compared to other methods of lane detection. Analysis of proposed optimized DOT with various edge detection techniques, various threshold levels, various sample dataset and various lane detection methods were done and the results are discussed in this paper. The proposed optimized DOT lane detection average processing time increases by 9.21 % when compared to previous Dynamic Origin Technique (DOT) and 59.09 % compared to traditional hough transform.
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Authors and Affiliations

P. Maya
1
C. Tharini
1

  1. B S Abdur Rahman Crescent Institute of Science and Technology, Chennai, India

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