Abstract
One of the major challenges faced by the automobile industries is to reduce the chance of occurring accidents and also to enhance the production of the safest automobiles. Even though many safety devices are available in the vehicles, the highest fatal terrific accidents occur on curved roads and junctions at nighttime. Also, the accidents occur due to glare from the fore coming vehicles. Because, in most of the cases, late recognition of objects in the zone plays a key role, and this happens due to improper forward lighting. So the main aim of this research is to provide enhanced nighttime safety measures by developing steerable dynamic headlights by considering most of the cases such as glare, curved roads, hill curves, and junctions. Also to react optimally based on the surrounding environment by interpreting the surrounding properly, an intelligent system has been developed to control the optimal movement of the headlight. Different kinds of tests were done on critical parts of the system, in order to determine its accuracy, its response time, and the system impact. Finally, the results acquired from these various tests are found satisfactory. It is a low-cost setup with minor modification on the doom of the headlight which will prevent the accidents due to improper lighting at nighttime.
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References
Alcantarilla PF (2011) Automatic light beam controller for driver assistance. Mach Vis Appl 22(5):819–835
Tripathy AK, Kayande D, George J, John J, Jose B (2015) Wi Lights—a wireless solution to control headlight intensity. In: IEEE international conference on technologies for sustainable development (ICTSD-2015), 04–06 Feb 2015, Mumbai, India
Chen YL, Chiang CY (2010) Embedded vision-based nighttime driver assistance system. In: 2010 international symposium on computer communication control and automation (3CA), vol 2. IEEE, pp 199–203
Juric D, Loncaric S, A method for on-road night-time vehicle headlight detection and tracking. In: 2014 international conference on IEEE connected vehicles and expo (ICCVE), Vienna, 3–7 Nov 2014, pp 655–660
Luo F, Hu F (2014) A comprehensive survey of vision based vehicle intelligent front light system. Int J Smart Sens Intell Syst 7(2):701–723
Fossati A, Schonmann P, Fua P (2011) Real-time vehicle tracking for driving assistance. Mach Vis Appl 22(2):439–448
Li Y, Haas N, Pankanti S (2011) Intelligent headlight control using learning-based approaches. In: 2011 IEEE on intelligent vehicles symposium (IV). IEEE, pp 722–727
Lopez A, Hilgenstock J, Busse A, Baldrich R, Lumbreras F, Serrat J (2008) Nighttime vehicle detection for intelligent headlight control. In: Advanced concepts for intelligent vision systems. Springer, pp 113–124
Alsumady M, Alboon SA (2013) Intelligent automatic high beam light controller. J Act Passiv Electron Dev 1–8 (Old City Publishing, Inc.)
New Headlight Sensors Make Night Driving Safer, Road and travel magazine. [Online]. Available: http://www.roadandtravel.com/autoadvice/2007/highbeams.html
O’Malley R, Jones E, Glavin M (2010) Rear-lamp vehicle detection and tracking in low-exposure color video for night conditions. IEEE Trans Intell Transp Syst 11(2):453–462
Eum S, Jung HG (2013) Enhancing light blob detection for intelligent headlight control using lane detection. IEEE Trans Intell Transp Syst 14(2):1003–1011
Zhang W, Wu QMJ, Wang G, You (2012) Tracking and pairing vehicle headlight in night scenes. IEEE Trans Intell Transp Syst 13(1):140–153
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/
Lighting Assist—SmartBeam® [Online]. Available: https://www.gentex.com/automotive/products/forward-driving-assist
Mobileye Binary Headlamp Contol [Online]. Available: http://www.mobileye.com/technology/applications/head-lamp-control/binary-headlamp-contol/
Chen D-Y, Lin Y-H, Peng Y-J (2012) Nighttime brake-light detection by nakagami imaging. IEEE Trans Intell Transp Syst 13(4):1627–1637
Ahonen T, Hadid A, Pietikainen M (2004) Face recognition with local binary patterns. In Proc Eur Conf Comput Vis 469–481
Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In Proc IEEE Conf Comput Vis Pattern Recogn 1(12):886–893
Kuang H, Chong Y, Li Q, Zhenge C (2014) MutualCascade method for pedestrian detection. Neurocomputing 137:127–135
Dollar P, Zitnick CL (2015) Fast edge detection using structured forests. IEEE Trans Pattern Anal Mach Intell 37(1):1–1
Rajesh Kanna SK, Manigandan S (2012) Intelligent vision inspection system for IC engine head: an ANN approach. J Adv Mater Res 479(12):2242–2245
Rajesh Kanna SK, Vignesh S, Sivashankar P, Vishwanath (2017) Intelligent handbraking system using artificial neural network. Int J Emerg Technol Adv Eng 7(9):734–739
Vignesh, Rajesh Kanna SK, Lingaraj N (2017) Intelligent automated guided vehicle using visual servoing. Am J Eng Res 6(11):16–20
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Rajesh Kanna, S.K., Lingaraj, N., Sivasankar, P., Raghul Khanna, C.K., Mohanakrishnan, M. (2019). Optimizing Headlamp Focusing Through Intelligent System as Safety Assistance in Automobiles. In: Hiremath, S., Shanmugam, N., Bapu, B. (eds) Advances in Manufacturing Technology. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-13-6374-0_59
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DOI: https://doi.org/10.1007/978-981-13-6374-0_59
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