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An Improved Driver Assistance System for Detection of Lane Departure Under Urban and Highway Driving Conditions

  • Anuja VatsEmail author
  • Binoy B. NairEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 835)

Abstract

One of the major challenges on highways is to avoid an unintended departure from the lane. This paper proposes a lane departure warning system with the help of monocular vision. The efficiency of such a system is subject to clarity of lanes, weather conditions and also method of acquisition. This paper proposes a method of lane detection that is robust to stray edges within the frame. Canny edge detection is utilized on the pre-processed images to obtain maximal intensity edges, followed by lane detection using Hough transform. In this paper we try to utilize selective property of the edges obtained from canny edge detection to reduce noisy edges and improve the false positive rate. The system has been tested to be effective in fully illuminated as well as badly illuminated road conditions with satisfactory results. The average detection rate obtained is 95%.

Keywords

Lane detection Lane departure ADAS Edge detection Hough transform 

References

  1. 1.
    Lotfy, O.G., et al.: Lane departure warning tracking system based on score mechanism. In: Midwest Symposium Circuits Systems, October, pp. 16–19 (2017)Google Scholar
  2. 2.
    Kortli, Y., Marzougui, M., Atri, M.: Efficient implementation of a real-time lane departure warning system. In: 2016 International Image Processing, Application System, pp. 1–6 (2016)Google Scholar
  3. 3.
    Son, J., Yoo, H., Kim, S., Sohn, K.: Real-time illumination invariant lane detection for lane departure warning system. Expert Syst. Appl. 42(4), 1816–1824 (2015)CrossRefGoogle Scholar
  4. 4.
    Gaikwad, V., Lokhande, S.: Lane departure identification for advanced driver assistance. IEEE Trans. Intell. Transp. Syst. 16(2), 910–918 (2015)Google Scholar
  5. 5.
    Dai, J., Wu, L., Lin, H., Tai, W.: A driving assistance system with vision based vehicle detection techniquesGoogle Scholar
  6. 6.
    Li, Q., Chen, L., Li, M., Shaw, S.L., Nüchter, A.: A sensor-fusion drivable-region and lane-detection system for autonomous vehicle navigation in challenging road scenarios. IEEE Trans. Veh. Technol. 63(2), 540–555 (2014)CrossRefGoogle Scholar
  7. 7.
    Gurghian, A., Koduri, T., Bailur, S.V., Carey, K.J., Murali, V.N.: DeepLanes: end-to-end lane position estimation using deep neural networks. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 38–45 (2016)Google Scholar
  8. 8.
    Murugesh, R., Ramanadhan, U., Vasudevan, N., Devassy, A., Krishnaswamy, D., Ramachandran, A.: Smartphone based driver assistance system for coordinated lane change. In: 2015 International Conference on Connected Vehicles and Expo, ICCVE 2015 - Proceedings, pp. 385–386 (2016)Google Scholar
  9. 9.
    Jayanth Balaji, A., Harish Ram, D.S., Nair, B.B.: Machine learning approaches to electricity consumption forecasting in automated metering infrastructure (AMI) systems: an empirical study. In: Silhavy, R., Senkerik, R., Kominkova Oplatkova, Z., Prokopova, Z., Silhavy, P. (eds.) CSOC 2017. AISC, vol. 574, pp. 254–263. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-57264-2_26CrossRefGoogle Scholar
  10. 10.
    Nair, B.B., Kumar, P.K.S., Sakthivel, N.R., Vipin, U.: Clustering stock price time series data to generate stock trading recommendations: an empirical study. Expert Syst. Appl. 70, 20–36 (2017)CrossRefGoogle Scholar
  11. 11.
    Singh, A.K., John, B.P., Subramanian, S.V., Kumar, A.S., Nair, B.B.: A low-cost wearable Indian sign language interpretation system. In: International Conference on Robotics & Automation for Humanitarian Applications (2016)Google Scholar
  12. 12.
    John, A.A., Nair, B.B., Kumar, P.N.: Application of clustering techniques for video summarization – an empirical study. In: Silhavy, R., Senkerik, R., Kominkova Oplatkova, Z., Prokopova, Z., Silhavy, P. (eds.) CSOC 2017. AISC, vol. 573, pp. 494–506. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-57261-1_49CrossRefGoogle Scholar
  13. 13.
    Wang, J.G., Lin, C.J., Chen, S.M.: Applying fuzzy method to vision-based lane detection and departure warning system. Expert Syst. Appl. 37(1), 113–126 (2010)CrossRefGoogle Scholar
  14. 14.
    Duda, R.O., Hart, P.E.: Use of the Hough transform to detect lines and cures in pictures. Commun. Assoc. Comput. Mach. 15(1), 11–15 (1972)zbMATHGoogle Scholar
  15. 15.
    Wu, T., Ranganathan, A.: A practical system for road marking detection and recognition. In: IEEE Intelligent Vehicles Symposium Proceedings, pp. 25–30 (2012)Google Scholar
  16. 16.
    Aly, M.: Real time detection of lane markers in urban streets. In: IEEE Intelligent Vehicles Symposium, pp. 7–12 (2008)Google Scholar
  17. 17.
    Wikipedia Contributors: Traffic Collisions in India. Wikipedia, The Free Encyclopedia, 25 July 2017. Accessed 26 Dec 2017Google Scholar
  18. 18.
    Times of India. Lane Cutting Speeding Big Killers on Stretch in 2016. In timesofindia.indiatimes.com, 17 December 2017Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Electronics and Communications EngineeringAmrita School of Engineering, Amrita Vishwa VidyapeethamCoimbatoreIndia
  2. 2.SIERS Research Laboratory, Department of Electronics and Communications EngineeringAmrita School of Engineering, Amrita Vishwa VidyapeethamCoimbatoreIndia

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