Lane Geometry Estimation in Urban Environments Using a Stereovision System

This paper presents a lane detection system that combines stereovision-specific techniques with grayscale image processing for maximizing the robustness and applicability against the difficult conditions of the urban environment. The lane marking features are extracted using a fast and robust dark-light-dark transition detector that´s aware of the perspective effect. The clothoid lane model is matched to the extracted features using line segment fitting for two distance intervals, under special constraints that ensure correctness. Freeform lane border detection, independent on the geometry constraints, driven by lane marking features only, is used to solve the situations not suited for clothoid representation. The results of each detection method are fused together in a Kalman filter based framework.