Open AccessTunnel lane‐positioning system for autonomous driving cars using LED chromaticity and fuzzy logic system
Author(s) -
Jeong JaeHoon,
Byun GiSig,
Park Kiwon
Publication year - 2019
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.2018-0192
Subject(s) - global positioning system , position (finance) , fuzzy logic , computer science , shadow (psychology) , computer vision , chromaticity , positioning system , artificial intelligence , inertial navigation system , real time computing , orientation (vector space) , engineering , telecommunications , mathematics , psychology , structural engineering , finance , node (physics) , economics , psychotherapist , geometry
Currently, studies on autonomous driving are being actively conducted. Vehicle positioning techniques are very important in the autonomous driving area. Currently, the global positioning system (GPS) is the most widely used technology for vehicle positioning. Although technologies such as the inertial navigation system and vision are used in combination with GPS to enhance precision, there is a limitation in measuring the lane and position in shaded areas of GPS, like tunnels. To solve such problems, this paper presents the use of LED lighting for position estimation in GPS shadow areas. This paper presents simulations in the environment of three‐lane tunnels with LEDs of different color temperatures, and the results show that position estimation is possible by the analyzing chromaticity of LED lights. To improve the precision of positioning, a fuzzy logic system is added to the location function in the literature [1]. The experimental results showed that the average error was 0.0619 cm, and verify that the performance of developed position estimation system is viable compared with previous works.