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Reseach Article

Night-time Vehicle Detection for Automatic Headlight Beam Control

by Pushkar Sevekar, S. B. Dhonde
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 157 - Number 7
Year of Publication: 2017
Authors: Pushkar Sevekar, S. B. Dhonde
10.5120/ijca2017912737

Pushkar Sevekar, S. B. Dhonde . Night-time Vehicle Detection for Automatic Headlight Beam Control. International Journal of Computer Applications. 157, 7 ( Jan 2017), 8-12. DOI=10.5120/ijca2017912737

@article{ 10.5120/ijca2017912737,
author = { Pushkar Sevekar, S. B. Dhonde },
title = { Night-time Vehicle Detection for Automatic Headlight Beam Control },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2017 },
volume = { 157 },
number = { 7 },
month = { Jan },
year = { 2017 },
issn = { 0975-8887 },
pages = { 8-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume157/number7/26841-2017912737/ },
doi = { 10.5120/ijca2017912737 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:03:36.265128+05:30
%A Pushkar Sevekar
%A S. B. Dhonde
%T Night-time Vehicle Detection for Automatic Headlight Beam Control
%J International Journal of Computer Applications
%@ 0975-8887
%V 157
%N 7
%P 8-12
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A clear visibility of the road ahead is significant concern for safe nighttime driving. However, high beams are utilized less than sufficient on the roads since drivers are afraid of dazzling others. Subsequently, the smart programmed control of vehicles front lamp is of incredible significance. According to road accident surveys, majority of the accidents occur in dark. Visibility in dark is significant issue for safe driving. Therefore careless drivers continue using a high beam even though approaching vehicle is observed. These high beams create glare for approaching drivers which then causes temporary blindness. To solve this problem, nighttime vehicle detection has a great importance. This paper reviews various attempts made to solve the problem, need of study, currently implemented relevant systems and related work, different approaches to solve problem and various applications. The survey shows sensor based approaches cannot be used to satisfy real time requirements of a system. However, a simple and reliable solution needs to be developed so that, it can be implemented in each vehicle. An attempt has been made to present an image processing based system that detects vehicles and selects ideal beam so that accidents due to temporary blindness can be reduced.

References
  1. New Headlight Sensors Make Night Driving Safer, Road and Travel Magazine, 2007. [Online]. Available: http://www.roadandtravel.com/autoadvice/2007/highbeams.htm
  2. Feng Luo And Fengjian Hu , A Comprehensive Survey Of Vision Based Vehicle Intelligent Front Light System, International Journal On Smart Sensing And Intelligent Systems Vol. 7, No. 2, June 2014, pp. 701-723.
  3. Amiya Kumar Tripathy, Deepali Kayande, Joel George, Jerome John, Bejoy Jose , Wi Lights - A Wireless Solution To Control Headlight Intensity , IEEE International Conference on Technologies for Sustainable Development (ICTSD-2015), Feb. 04–06, 2015, Mumbai, India
  4. Mohammed Alsumady and Shadi. A. Alboon, Intelligent Automatic High Beam Light Controller, ©2013 Old City Publishing, Inc. J. of Active and Passive Electronic Devices, Vol. 00, pp. 1–8.
  5. Sungmin Eum and Ho Gi Jung, Enhancing Light Blob Detection for Intelligent Headlight Control Using Lane Detection, IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. 14, NO. 2, JUNE 2013, pp 1003-1011
  6. P. F. Alcantarilla, Automatic LightBeam Controller for driver assistance , Springer-Verlag Machine Vision and Applications September 2011, Volume 22, Issue 5 , pp 819-835
  7. Darko Juri´c and Sven Lonˇcari´c, A Method for On-road Night-time Vehicle Headlight Detection and Tracking, IEEE Connected Vehicles and Expo (ICCVE), 2014 International Conference Vienna 3-7 Nov. 2014 , pp 655 – 660.
  8. Y. Li, N. Haas, and S. Pankanti, “Intelligent headlight control using learning-based approaches,” in Intelligent Vehicles Symposium (IV),2011 IEEE. IEEE, 2011, pp. 722–727.
  9. A. L´opez, J. Hilgenstock, A. Busse, R. Baldrich, F. Lumbreras, and J. Serrat, “Nighttime vehicle detection for intelligent headlight control,” in Advanced Concepts for Intelligent Vision Systems. Springer, 2008, pp. 113–124.
  10. Y.-L. Chen and C.-Y. Chiang, “Embedded vision-based nighttime driver assistance system,” in Computer Communication Control and Automation (3CA), 2010 International Symposium on, vol. 2. IEEE, 2010, pp. 199–203.
  11. A. Fossati, P. Sch´onmann, and P. Fua, “Real-time vehicle tracking for driving assistance,” Machine Vision and Applications, vol. 22, no. 2, pp. 439–448, 2011.
  12. R. O’Malley, E. Jones, and M. Glavin, “Rear-lamp vehicle detection and tracking in low-exposure color video for night conditions,” IEEE Trans.Intell. Transp. Syst., vol. 11, no. 2, pp. 453–462, Jun. 2010.
  13. W. Zhang, Q. M. J. Wu, G. Wang, and X. You, “Tracking and pairing vehicle headlight in night scenes,” IEEE Trans. Intell. Transp. Syst., vol. 13, no. 1, pp. 140–153, Mar. 2012.
  14. Multibeam LED brings light into the darkness [Online]. Available: https://www.mercedes-benz.com/en/mercedes-benz/innovation/multibeam-led-brings-light-into-the-darkness/
  15. Lighting Assist - SmartBeam® [Online]. Available: https://www.gentex.com/automotive/products/forward-driving-assist
  16. Mobileye Binary Headlamp Contol [Online]. Available:  http://www.mobileye.com/technology/applications/head-lamp-control/binary-headlamp-contol/
  17. D.-Y. Chen et al., “Nighttime Brake-Light detection by nakagami imaging,” IEEE Trans. Intelligent Transportation Systems, vol. 13, no. 4, pp. 1627 – 1637, 2012.
  18. T. Ahonen et al., “Face recognition with local binary patterns,” in Proc.European Conference on Computer Vision, 2004, pp. 469–481.
  19. N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, no. 12, 2005, pp. 886–893.
  20. H. Kuang et al., “MutualCascade method for pedestrian detection,” Neurocomputing, vol. 137, pp. 127–135, 2014.
  21. P. Doll´ar and C. L. Zitnick, “Fast edge detection using structured forests,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 37,no. 1, pp. 1–1, 2015.
Index Terms

Computer Science
Information Sciences

Keywords

Headlight control Nighttime vehicle detection beam control Light blob detection Low beam High beam.