A new approach for camera supported machine learning algorithms based dynamic headlight model's design

Year 2022, , 117 - 128, 30.12.2022

Şafak Yaşar , Mustafa Şahin , Onur Akar

https://doi.org/10.18245/ijaet.1168186

Abstract

Traffic accidents continue to be a significant issue in modern society. Accidents usually happen on dark, mountainous, narrow, steep and curved roadways. One of the primary causes of such accidents is the drivers’ weak sight brought on by the headlights of moving vehicles. In this study, a dynamic headlight model was designed using camera supported machine learning algorithms to improve the drivers’ vision during night drive. In this design, the issues of enabling a lighting field supported by image processing programmed with machine learning, dynamic adjustment of the high beam headlights’ LED cells in response to the vehicle approaching from the opposite direction, traffic-sign recognition system, lane-keeping system, and automatic adjustment of headlight angles were addressed. In this direction, a novel dynamic headlight model that will reduce the risk of accidents caused by lighting was presented, and its analyses were performed.

Keywords

Vehicle headlight system, Machine learning, Traffic-sign recognition, Lane-keeping, Vehicle recognition

References

• ScienceDaily, “Traffic accidents, Avoiding Rear-end Collisions”, Online available: (24.08.2022), http://www.sciencedaily.com
• Vorndran, I., “Unfallgeschehen im Straenverkehr 2006”, Wirtschaft und Statistik 2007, Statistisches Bundesamt, Wiesbaden, Vol. 7, 2007, pp.679-687.
• Lee, YM., Sheppard, E., “The effect of lighting conditions and use of headlights on drivers’ perception and appraisal of approaching vehicles at junctions”, Ergonomics, Vol. 61, No. 3,2018, pp. 444-455, https://doi.org/10.1080/00140139.2017.1364790
• Balaji, RD, “A Case study on Automatic Smart Headlight System for Accident Avoidance”, Int. J. Comput. Commun. Informatics, Vol. 2 Iss. 1, 2020, pp.70–77, https://doi.org/10.34256/ijcci2012
• Romansky, ML., Plummer, RW., Neumann, ES. “Environmental Stressors as Causal Factors for Driver Fatigue”, Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 1979, pp.452–455, https://doi.org/10.1177/107118137902300169
• Adrian, J., Hue, D., Porte, S., Brun, JL., “Validation of the driver ecological glare test”, Journal of Safety Research, Journal of Safety Research 72, 2020, pp.139–143
• Büthorn, R., Tadjine, H.H., Auerbach, B., Schulze, K., “Advanced Headlight System”, 3D High Beam, 2013, pp. 77–88, https://doi.org/10.1007/978-3-319-00476-1_8.
• Reagan, IJ., Frischmann, T., Brumbelow , ML., “Test track evaluation of headlight glare associated with adaptive curve HID, fixed HID, and fixed halogen low beam headlights” Ergonomics, Vol. 59, No. 12, 2016, pp.1586–1595, http://dx.doi.org/10.1080/00140139.2016.1154986
• Gil, PA., Santos-Berbel, CD., Castro, M., “Driver glare exposure with different vehicle frontlighting systems”, Journal of Safety Research, Volume 76, February 2021, pp.228-237, https://doi.org/10.1016/j.jsr.2020.12.018
• Chen, YS., Chiu, SC., “New method of automatic control for vehicle headlights”, Optik 157, 2018, pp.718-723, https://doi.org/10.1016/j.ijleo.2017.11.137.
• Chen, Y.L., Chiang, C.Y., “Embedded vision-based nighttime driver assistance system, IEEE, International Symposium on Computer, Communication, Control and Automation, 2010, pp. 199–203, https://doi.org/10.1109/3CA.2010.5533586.
• Connell, JH., Herta, BW., Pankanti, S., Hess, H., Pliefke, S., “A fast and robust intelligent headlight controller for vehicles, in: IEEE Intell. Veh. Symp.,Proc., 2011: pp. 703–708, https://doi.org/10.1109/IVS.2011.5940492.
• Tripathy, AK., Kayande, D. , George,J., John, J., Jose, B., “Wi lights - A wireless solution to control headlight intensity”, in: Proc. - Int. Conf. Technol. Sustain. Dev. ICTSD 2015, Institute of Electrical and Electronics Engineers Inc., 2015, https://doi.org/10.1109/ICTSD.2015.7095866.
• Mayr, J., Giracoglu , C., Unger, C., Tombari , F., “Headlight Range Estimation for Autonomous Driving using Deep Neural Networks”, 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France. June 9-12, 2019, pp.2251-2258. https://doi.org/10.0/Linux-x86_64.
• Vijay, G., Ramanarayan, MN., Arunkumar PC., “Design and Integration of Lane Departure Warning, Adaptive Headlight and Wiper system for Automobile Safety”, IEEE, 2019 4th International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT-2019), May 17th & 18th 2019.
• Muhammad, F., Yanto, DD., Martiningsih, W., Noverli, V., Wiryadinata, R., “Design of Automatic Headlight System Based on Road Contour and Beam from Other Headlights”, Proceeding - 2020 2nd Int. Conf. Ind. Electr. Electron. ICIEE 2020, Institute of Electrical and Electronics Engineers Inc., 2020, pp. 112–115, https://doi.org/10.1109/ICIEE49813.2020.9276906.
• Görmer, S., Müller, D., Hold, S., Meuter, M., Kummert, A., “Vehicle Recognition and TTC Estimation at Night based on Spotlight Pairing”, Proceedings of the 12th International IEEE Conference on Intelligent Transportation Systems, St. Louis, MO, USA, October 3-7, 2009, pp.196-201
• Chen, YL., Chen, YH., Chen, CJ, Wu, BF., “Nighttime Vehicle Detection for Driver Assistance and Autonomous Vehicles”, IEEE, Proceedings of the 18th International Conference on Pattern Recognition (ICPR'06), 2006, pp.1051-4651, https://doi.org/10.1109/ICPR.2006.858.
• Connell, JH., Herta, BW., Pankanti, S., Hess, H., Pliefke, S., “A Fast and Robust Intelligent Headlight Controller for Vehicles” IEEE Intelligent Vehicles Symposium (IV), 2011, pp.703-708, https://doi.org/10.1109/IVS.2011.5940492.
• Activation Functions in Neural Networks, Online available: (20.08.2022), https://towardsdatascience.com/activation-functions-neural-networks-1cbd9f8d91d6
• Andrei, MA., Boiangiu, CA., Tarbă, N., Voncilă, ML., “Robust lane detection and tracking algorithm for steering assist systems”, Machines. 10, 2022, https://doi.org/10.3390/machines1001001.
• Sekehravani, EA., Babulak, E., Masoodi, M., “Implementing canny edge detection algorithm for noisy image”, Bull. Electr. Eng. Informatics. 9, 2020, pp. 404–1410, https://doi.org/10.11591/eei.v9i4.1837.
• Wolf, C., Jolion, JM., Chassaing, F., “Text localization, enhancement and binarization in multimedia documents,” IEEE, 2002 International Conference on Pattern Recognition, Quebec City, Quebec, Canada, vol. 2, 2002, pp. 1037-1040, https://doi.org/10.1109/ICPR.2002.1048482
• Fokkinga, M., “The Hough transform”, J. Funct. Program. 21, 2011, pp.129–133, https://doi.org/10.1017/S0956796810000341.
• Radius of curvatur, Online available: (24.08.2022), https://www.intmath.com/applications-differentiation/8-radius-curvature.php.