Abstract
Lane detection is an essential part of safety assurance in intelligent vehicle and advanced driver assistance systems. Despite many methods having been proposed, there still remain challenges such as complex road surface and large curvature. In this paper, we present a robust lane detection method under structured roads to solve these issues. The method contains two parts: straight line detection in near field and curve matching in far field. Instead of generating top-view image by inverse perspective mapping (IPM), we propose a new form of IPM application to reduce noise that we only take advantage of sub-pixel-level spatial relations and project line segments obtained by line segments detector to top-view image. Then, we apply density-based spatial clustering of applications with noise to clustering segments and design a fusion method to extract the optimal lines combination from clusters. Finally, a weighted hyperbolic model is proposed to finish curve fitting. The results of experiment indicate that the method has robust performance in complex environment.
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References
Aly M (2008) Real time detection of lane markers in urban streets. In: IEEE intelligent vehicles symposium, proceedings, pp 7–12
Aly M (2009) Caltech lanes dataset. http://vision.caltech.edu/malaa/datasets/caltech-lanes. Accessed 10 May 2017
An X, Shang E, Song J et al (2013) Real-time lane departure warning system based on a single FPGA. Eurasip J Image Video Process. https://doi.org/10.1186/1687-5281-2013-38
Andrade D, Adamshuk R, Omoto W et al (2017) Lane detection using orientation of gradient and vehicle network signals. SAE Tech Pap. https://doi.org/10.4271/2017-01-0042.copyright
Chiu KY, Lin SF (2005) Lane detection using color-based segmentation. In: IEEE intelligent vehicles symposium, proceedings, pp 706–711
Cicchino JB (2017) Effects of lane departure warning on police-reported crash rates. J Saf Res. https://doi.org/10.1016/j.jsr.2018.05.006
Grompone von Gioi R, Jakubowicz J, Morel J-M, Randall G (2012) LSD: a line segment detector. Image Process Line. https://doi.org/10.5201/ipol.2012.gjmr-lsd
Hahsler M, Piekenbrock M (2017) dbscan: density based clustering of applications with noise (DBSCAN) and related algorithms. R Packag. version
Hsiao PY, Yeh CW, Huang SS, Fu LC (2009) A portable vision-based real-time lane departure warning system: day and night. IEEE Trans Veh Technol. https://doi.org/10.1109/tvt.2008.2006618
Jung S, Youn J, Sull S (2015) Efficient lane detection based on spatiotemporal images. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/tits.2015.2464253
Kant S, Mahara T, Kumar Jain V et al (2018) LeaderRank based k-means clustering initialization method for collaborative filtering. Comput Electr Eng. https://doi.org/10.1016/j.compeleceng.2017.12.001
Lan M, Rofouei M, Soatto S, Sarrafzadeh M (2009) SmartLDWS: a robust and scalable lane departure warning system for the smartphones. In: IEEE conference on intelligent transportation systems, proceedings, ITSC, pp 108–113
Lee C, Member S, Moon J (2018) Robust lane detection and tracking for real-time applications. IEEE Trans Intell Transp Syst 1:1–6
Liu S, Cheng X, Fu W et al (2014) Numeric characteristics of generalized M-set with its asymptote. Appl Math Comput 243:767–774. https://doi.org/10.1016/j.amc.2014.06.016
Liu S, Pan Z, Song H (2017) Digital image watermarking method based on DCT and fractal encoding. IET Image Process 11:815–821. https://doi.org/10.1049/iet-ipr.2016.0862
Liu G, Liu S, Muhammad K et al (2018) Object tracking in vary lighting conditions for fog based intelligent surveillance of public spaces. IEEE Access. https://doi.org/10.1109/access.2018.2834916
Maini R, Aggarwal DH (2009) Study and comparison of various image edge detection techniques. Int J Image Process 3:1–11
Mammeri A, Boukerche A, Tang Z (2016) A real-time lane marking localization, tracking and communication system. Comput Commun 73:132–143. https://doi.org/10.1016/j.comcom.2015.08.010
McCall JC, Trivedi MM (2006) Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation. IEEE Trans Intell Transp Syst 7:20–37
Narote SP, Bhujbal PN, Narote AS, Dhane DM (2018) A review of recent advances in lane detection and departure warning system. Pattern Recognit. https://doi.org/10.1016/j.patcog.2017.08.014
Niu J, Lu J, Xu M et al (2016) Robust lane detection using two-stage feature extraction with curve fitting. Pattern Recognit 59:225–233
Ozgunalp U, Fan R, Ai X, Dahnoun N (2017) Multiple lane detection algorithm based on novel dense vanishing point estimation. IEEE Trans Intell Transp Syst 18:621–632. https://doi.org/10.1109/tits.2016.2586187
Piao J, Shin H (2017) Robust hypothesis generation method using binary blob analysis for multi-lane detection. IET Image Process 11:1210–1218. https://doi.org/10.1049/iet-ipr.2016.0506
Ruyi J, Reinhard K, Tobi V, Shigang W (2011) Lane detection and tracking using a new lane model and distance transform. Mach Vis Appl. https://doi.org/10.1007/s00138-010-0307-7
Sahu B, Kumar Sa P, Bakshi S, Sangaiah AK (2018) Reducing dense local feature key-points for faster iris recognition. Comput Electr Eng 70:939–949
Saudi A, Teo J, Sulaiman J (2008) Fast lane detection with randomized hough transform. In: 2008 international symposium on information technology, pp 1–5
Shin BS, Tao J, Klette R (2015) A superparticle filter for lane detection. Pattern Recognit. https://doi.org/10.1016/j.patcog.2014.10.011
Song W, Fu M, Yang Y et al (2017) Real-Time lane detection and forward collision warning system based on stereo vision. IEEE Intell Veh Symp Proc 18:493–498. https://doi.org/10.1109/ivs.2017.7995766
Taubel G, Sharma R, Yang J (2014) An experimental study of a lane departure warning system based on the optical flow and Hough transform methods. WSEAS Trans Syst 13:105–115
Tian Y, Cui W, Li X, Chen L (2018) A robust lane detection method based on vanishing point estimation. Procedia Comput Sci 131:354–360
Tu C, van Wyk BJ, Hamam Y et al (2013) Vehicle position monitoring using hough transform. IERI Procedia 4:316–322. https://doi.org/10.1016/j.ieri.2013.11.045
Wang X et al (2011) Robust lane detection based on gradient-pairs constraint. In: 2011 30th Chinese control conference (CCC)
Wang J, An X (2010) A multi-step curved lane detection algorithm, based on hyperbola-pair model. In: 2010 IEEE international conference on automation and logistics, ICAL 2010, pp 132–137
Wang Y, Shen D, Teoh EK (2000) Lane detection using spline model. Pattern Recognit Lett 21:677–689. https://doi.org/10.1016/s0167-8655(00)00021-0
Wang Y, Teoh EK, Shen D (2004) Lane detection and tracking using B-Snake. Image Vis Comput 22:269–280. https://doi.org/10.1016/j.imavis.2003.10.003
Wu PC, Chang CY, Lin CH (2014) Lane-mark extraction for automobiles under complex conditions. Pattern Recognit 47:2756–2767. https://doi.org/10.1016/j.patcog.2014.02.004
Yang T, Long X, Sangaiah AK et al (2018) Deep detection network for real-life traffic sign in vehicular networks. Comput Netw. https://doi.org/10.1016/j.comnet.2018.02.026
Yu G, Wang Z, Wu X et al (2017) Efficient lane detection using deep lane feature extraction method. SAE Int J Passeng Cars Electron Electr Syst. https://doi.org/10.4271/2017-01-1970
Zhang Z (2000) A flexible new technique for camera calibration. IEEE Trans Pattern Anal Mach Intell 22:1330–1334. https://doi.org/10.1109/34.888718
Zhao K, Meuter M, Nunn C et al (2012) A novel multi-lane detection and tracking system. In: IEEE intelligent vehicles symposium, proceedings, pp 1084–1089
Acknowledgements
The work described in this paper was funded by Science and Technology Development Plan of Jilin Province (20170204020GX) and National Natural Science Foundation of China under Grant U1564211 and 51805203.
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Communicated by A. K. Sangaiah, H. Pham, M.-Y. Chen, H. Lu, F. Mercaldo.
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Li, W., Qu, F., Wang, Y. et al. A robust lane detection method based on hyperbolic model. Soft Comput 23, 9161–9174 (2019). https://doi.org/10.1007/s00500-018-3607-x
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DOI: https://doi.org/10.1007/s00500-018-3607-x