Robust Calculation of Ego-Vehicle Corridors for Vision-Based DAS

An important component of driver assistance systems (DAS) is lane detection, and has been studied since the 1990s. However, improving and generalizing lane detection solutions remains to be a challenging task until recently. A (physical) lane is defined by road boundaries or various kinds of lane marks, and this is only partially applicable for modeling the space an ego-vehicle is able to driving in. This paper proposes a concept of a (virtual) corridor for modeling this space. A corridor depends on information available about the motion of the ego-vehicle, as well as about the (physical) lane. This paper suggests robust corridor detection using hypothesis testing based on maximum a posterior (MAP) estimation. Then, boundary selection and road patch extension are applied as post-processing. Furthermore, a simple but efficient corridor tracking method is also discussed. This paper also informs the readers about experiments using images of some challenging road situations illustrating the usefulness of the proposed corridor detection and tracking scheme.