Visual ego-vehicle lane assignment using Spatial Ray features

Abstract

Assigning the ego-vehicle to a lane is not only beneficial for navigation but will be an essential element in future Advanced Driver Assistance Systems. This paper describes an approach for ego-lane index estimation using only a monocular camera and no additional sensing equipment like, e.g., the typically employed GPS and Inertial Measurement Unit. Key aspect of the approach are SPatial RAY (SPRAY) features which represent the spatial layout of the road in the visual scene. The proposed method perceives a variety of local visual properties of the scene by means of base classifiers operating on patches extracted from camera images. The spatial arrangement of these local visual properties are captured using SPRAY features. With a boosting classifier trained on these features the ego-lane index is obtained. The system is evaluated on low traffic density and complementary to an object-based approach suitable for heavy traffic. In the conducted experiments, the proposed approach reaches recognition rates of 93% to 97% on individual highway images without applying any kind of temporal filtering.