Autonomous lane detection on hilly terrain for perception-based navigation applications

References

• Li Q, Chen L, Li M, et al. A sensor-fusion drivable-region and lane-detection system for autonomous vehicle navigation in challenging road scenarios. IEEE Trans Veh Technol. 2014;63(2):540–555. doi: 10.1109/TVT.2013.2281199
   Web of Science ®Google Scholar
• Shafique M, Fahim M, Pydipogu P. Robust lane detection and object tracking in relation to the intelligence transport system. Elect Eng. 2013. Available from: http://www.digitalamedier.bth.se/fou/cuppsats.nsf/all/fb2ff9939df42fedc1257c06002cebc4/$file/BTH2013Shafique.pdf
   Google Scholar
• Manoharan K, Daniel P. Survey on various lane and driver detection techniques based on image processing for hilly terrain. IET Image Proc. 2018;12(9):1567–1576. doi: 10.1049/iet-ipr.2017.0864
   Web of Science ®Google Scholar
• McCall JC, Wipf DP, Trivedi MM, et al. Lane change intent analysis using robust operators and sparse Bayesian learning. IEEE Trans Intell Transp Syst. 2007;8(3):431–440. doi: 10.1109/TITS.2007.902640
   Web of Science ®Google Scholar
• Bertozzi M, Broggi A. GOLD : A parallel real-time stereo vision system for generic obstacle and lane detection. IEEE Trans Image Process. 1998;7(1):62–81. doi: 10.1109/83.650851
   PubMed Web of Science ®Google Scholar
• Yi S-C, Chen Y-C, Chang C-H. A lane detection approach based on intelligent vision. Elsevier, Comput Electr Eng. 2015;42:23–29. doi: 10.1016/j.compeleceng.2015.01.002
   Web of Science ®Google Scholar
• Assidiq AA, Khalifa OO, Islam MR, et al. Real time lane detection for autonomous vehicles. International conference on Computer and Communication Engineering, Kualalumpur, Malaysia; 2008. p. 82–88. doi:10.1109/ICCCE.2008.4580573
   Google Scholar
• Jung H, Min J, Kim J. An efficient lane detection algorithm for lane departure detection. IEEE Intelligent Vehicles Symposium, Proceedings; 2013. p. 976–981. doi:10.1109/IVS.2013.6629593
   Google Scholar
• D’Cruz C, Jia Zou J. Lane detection for driver assistance and intelligent vehicle applications. IEEE International symposium on Communications and Information Technologies (ISCIT); 2007. p. 1291–1296.
   Google Scholar
• Wang Y, Shen D, Teoh EK. Lane detection using spline model. Pattern Recognit Lett. 2000;21(8):677–689. doi: 10.1016/S0167-8655(00)00021-0
   Web of Science ®Google Scholar
• Wang Y, Teoh EK, Shen D. Lane detection and tracking using B-snake. Image Vis Comput. 2004;22(4):269–280. doi: 10.1016/j.imavis.2003.10.003
   Web of Science ®Google Scholar
• Zhou S, Jiang Y, Xi J, et al. A novel lane detection based on geometrical model and Gabor filter. 2010 IEEE Intelligent Vehicles Symposium; 2010. p. 59–64. doi:10.1109/IVS.2010.5548087
   Google Scholar
• Cheng H-Y, Jeng B-S, Tseng P-T, et al. Lane detection With moving vehicles in The traffic Scenes. IEEE Trans Intell Transp Syst. 2006;7(4):571–582. doi: 10.1109/TITS.2006.883940
   Web of Science ®Google Scholar
• Aly M. Real time detection of lane markers in urban streets. IEEE Intelligent Vehicles Symposium, Proceedings; 2008. p. 7–12. doi:10.1109/IVS.2008.4621152
   Google Scholar
• Kaur G, Chhabra A. Curved lane detection using improved Hough transform and CLAHE in a Multi-Channel ROI. Int J Comput Appl. 2015;122(13):32–35. Available from: http://research.ijcaonline.org/volume122/number13/pxc3905011.pdf
   Google Scholar
• Tsai L, Hsieh J, Chuang C, et al. Lane detection using directional random walks. 2008 IEEE Intelligent Vehicles Symposium. p. 303–306. doi:10.1109/IVS.2008.4621271
   Google Scholar
• Saudi A, Teo J, Sulaiman J. Fast lane detection with randomized hough transform. 2008 International Symposium on Information Technology; 2008 p. 1–5. doi:10.1109/ITSIM.2008.4631879
   Google Scholar
• Lai AHS, Yung NHC. Lane detection by orientation and length discrimination. IEEE Trans Syst, Man, Cybern, B: Cybern. 2000;30(4):539–548. doi: 10.1109/3477.865171
   PubMed Web of Science ®Google Scholar
• Mccall JC, Trivedi MM. Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation. IEEE Trans Intell Transp Syst. 2006;7(1):20–37. doi: 10.1109/TITS.2006.869595
   Web of Science ®Google Scholar
• Li C, Dai B, Wang R, et al. Multi-lane detection based on omnidirectional camera using anisotropic steerable filters. IET Intel Transport Syst. 2016;10:298–307. doi: 10.1049/iet-its.2015.0144
   Web of Science ®Google Scholar
• An X, Shang E, Song J, et al. Real-time lane departure warning system based on a single FPGA. EURASIP Journal on Image and Video Processing; 2013. Available from: http://jivp.eurasipjournals.com/content/2013/1/38
   Google Scholar
• Guan H, Xingang W, Wenqi W, et al. Real-time lane-vehicle detection and tracking system. IEEE Proc Control and Decision Conference (CCDC); 2016, p. 4438–4443.
   Google Scholar
• Wu CF, Lin CJ, Lin HY, et al. Adjacent lane detection and lateral vehicle distance measurement using vision-based neuro-fuzzy approaches. J Appl Res Technol. 2013;11:251–258. doi: 10.1016/S1665-6423(13)71535-9
   Google Scholar
• Chiang H-H, Chen Y-L, Wu BF, et al. Embedded driver-assistance system using multiple sensors for safe overtaking maneuver. IEEE Syst J. 2014;8(3):681–698. doi: 10.1109/JSYST.2012.2212636
   Web of Science ®Google Scholar
• Guo C, Meguro J, Kojima Y, et al. A Multimodal ADAS system for Unmarked urban scenarios based on road Context Understanding. IEEE Trans Intell Transp Syst. 2015;16(4):1690–1704. doi: 10.1109/TITS.2014.2368980
   Web of Science ®Google Scholar
• Dubey A. Robust and real time detection of curvy lanes (curves) having desired slopes for driving assistance and autonomous vehicles. Computer science and information technology; 2015. p. 107–116. doi:10.5121/csit.2015.50111
   Google Scholar
• Amaradi P, Sriramoju N, Dang L, et al. Lane following and obstacle detection techniques in autonomous driving vehicles. IEEE International Conference on Electro Information Technology (EIT); 2016. p. 674–679.
   Google Scholar
• Tang T, Lu X, Wei P, et al. A lane detection method for freeway aerial videos. IEEE Proceedings – 2015 8th International Congress on Image and Signal Processing, CISP 2015; 2015. p. 69–74. doi:10.1109/CISP.2015.7407852
   Google Scholar
• Manoharan K, Daniel P. Image processing-based framework for continuous lane recognition in mountainous roads for driver assistance system. J Electron Imaging. 2017;26(6):063011. doi:10.1117/1.JEI.26.6 doi: 10.1117/1.JEI.26.6.063011
   Web of Science ®Google Scholar
• Gonzalez RC, Woods RE, Scott AH. Digital image processing. 3rd ed. Upper Saddle River (NJ): Pearson Prentice Hall; 2008.
   Google Scholar