Vision-based lane boundaries following for autonomous vehicle guidance

This paper proposes a technique for lane boundaries detection and following, which can be used in a driver assistance system. This technique is destined only to painted road with slow curvature. Then, a linear model is used to obtain robust information about vehicle position and orientation compared to the road boundaries. Based on the gray level images our approach uses the Radon transform to extract the white lane markers with exploiting the contrast between these markers and road surface. To ensure robustness to the lane following process, we assume a temporal correlation between the successive images. Experimental results indicate that the proposed approach can fit lane boundaries in the presence of several image artifacts, such as sparse shadows and lighting changes. Introduction The road accidents are currently among the principal causes of death in the world. They occur in various situations, and for various reasons. A particular type of these accidents is called Single Vehicle Road Departure (SVRD) that involves a single vehicle, which departs the road and then impacts something such as a tree or a bridge abutment. The degradation of driver performance during a long driving task can be a direct cause of SVRD. A fatigued or impaired driver during highway driving is an obvious example. Recently driver distraction (e.g., cellular phones, navigation systems) has received considerable attention. So, a great deal of research in the domain of transport systems has been conducted to improve the safety conditions by the partial automation of some driving tasks. Among these tasks, the lane recognition take an important part in each drive assistance system and autonomous vehicle, which provides information such as lane structure and vehicle position relative to the lane. Thanks to the great deal of information it can deliver, computer vision become a powerful means for sensing the environment and has been widely used in lane boundaries detection and following. A common theme among many previous lane trackers is the use of lane markers as the primary feature for tracking and consequently there are a number of techniques for lane marker extraction. The General Obstacle and Lane Detection system (GOLD) used in the ARGO vehicle at the University of Parma exploits a flat road assumption to help simplify the problem of lane tracking (M. Bertozzi, A. Broggi, 1998). The system transforms stereo image pairs into a common bird’s eye view using an inverse projective mapping and uses a pattern matching technique to detect lane markings on the road. The group at the University of Michigan has produced two novel contributions to the field of lane tracking: LOIS and LANA (Kreucher et al, 1998; Kreucher & Lakshmanan, 1999), where a parametric family of deformable templates is used to identify lanes, with the likelihood measure identifying how well a particular parameterization matches the observation. Moreover, a prior model constrains the possible locations of the lane based on the lane location from the previous frame. Pomerleau and Jochem propose in (Pomerleau and Jochem, 1996) a RALPH system, used to control the lateral position of an autonomous vehicle. It is based on the processing of the image portion corresponding to road about 20-70m ahead the vehicle, depending on the vehicle’s speed and obstacles presence. It uses a matching technique that adaptively adjusts and aligns a template to the averaged scanline intensity profile in order to determine the lane’s curvature and lateral offsets. A lane detection algorithm destined to painted or unpainted road has been proposed by Ran et al (Ran & Xianghong Liu, 2000). In this algorithm color cues were used to conduct image segmentation and remove the shadow of the road. Assuming that the lanes are normally long and smooth curves, then its can considered as straight lines within a reasonable range for vehicle safety. The lanes Visionbased lane boundaries