In the last few years, driver assistance systems are increasingly being investigated in the automotive field to provide a higher degree of safety and comfort. Lane position determination plays a critical role toward the development of autonomous and computer-aided driving. This paper presents an accurate and robust method for detecting road markings with applications to autonomous vehicles and driver support. Much like other lane detection systems, ours is based on computer vision and Hough transform. The proposed approach, however, is unique in that it uses fuzzy reasoning to combine adaptively geometrical and intensity information of the scene in order to handle varying driving and environmental conditions. Since our system uses fuzzy logic operations for lane detection and tracking, we call it “FLane.” This paper also presents a method for building the initial lane model in real time, during vehicle motion, and without any a priori information. Details of the main components of the FLane system are presented along with experimental results obtained in the field under different lighting and road conditions.
FLane: An Adaptive Fuzzy Logic Lane Tracking System for Driver Assistance
REINA, GIULIO;
2011-01-01
Abstract
In the last few years, driver assistance systems are increasingly being investigated in the automotive field to provide a higher degree of safety and comfort. Lane position determination plays a critical role toward the development of autonomous and computer-aided driving. This paper presents an accurate and robust method for detecting road markings with applications to autonomous vehicles and driver support. Much like other lane detection systems, ours is based on computer vision and Hough transform. The proposed approach, however, is unique in that it uses fuzzy reasoning to combine adaptively geometrical and intensity information of the scene in order to handle varying driving and environmental conditions. Since our system uses fuzzy logic operations for lane detection and tracking, we call it “FLane.” This paper also presents a method for building the initial lane model in real time, during vehicle motion, and without any a priori information. Details of the main components of the FLane system are presented along with experimental results obtained in the field under different lighting and road conditions.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
