Automatic LightBeam Controller for driver assistance

• Published in: Machine vision and applications Vol. 22; no. 5; pp. 819 - 835
• Main Authors: Alcantarilla, P. F., Bergasa, L. M., Jiménez, P., Parra, I., Llorca, D. F., Sotelo, M. A., Mayoral, S. S.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer-Verlag 01.09.2011; Springer Nature B.V
• Subjects: Algorithms; Automobiles; Cameras; Communications Engineering; Computer Science; Controllers; Drivers; Driving conditions; Headlamps; Headlights; Image classification; Image detection; Image processing; Image Processing and Computer Vision; Light sources; Networks; Night; Original Paper; Pattern Recognition; Sequences; Support vector machines; Traffic signs; Urban areas; Vehicles; Vision systems; Windshields; Support vector machines; Computer vision; Head-lights detection; Tail-lights detection; Driver-assistance systems
• ISSN: 0932-8092; 1432-1769
• DOI: 10.1007/s00138-011-0327-y

In this article, we present an effective system for detecting vehicles in front of a camera-assisted vehicle (preceding vehicles traveling in the same direction and oncoming vehicles traveling in the opposite direction) during night-time driving conditions in order to automatically change vehicle head lights between low beams and high beams avoiding glares for the drivers. Accordingly, high beams output will be selected when no other traffic is present and will turn low beams on when other vehicles are detected. In addition, low beams output will be selected when the vehicle is in a well lit or urban area. LightBeam Controller is used to assist drivers in controlling vehicle’s beams increasing its correct use, since normally drivers do not switch between high beams and low beams or vice versa when needed. Our system uses a B&W forward looking micro-camera mounted in the windshield area of a C4-Picasso prototype car. Image processing techniques are applied to analyse light sources and to detect vehicles in the images. Furthermore, the system is able to classify between vehicle lights and road signs reflections or nuisance artifacts by means of support vector machines. The algorithm is efficient and able to run in real time. The system has been tested with different video sequences (more than 7 h of video sequences) under real night driving conditions in different roads of Spain. Experimental results, a comparison with other representative state of the art methods and conclusions about the system performance are presented.