Probabilistic Approach for Automated Reasoning for Lane Identification in Intelligent Vehicles

Abstract

This paper proposes a probabilistic model for automated reasoning for identifying the lane on which the vehicle is driving on. The solution is based on the visual information from an on-board stereo-vision camera and a priori information from an extended digital map. The visual perception system provides information about on-the-spot detected lateral landmarks, as well as about other important traffic elements such as other vehicles. The proposed extended digital map provides lane level detail information about the road infrastructure. An Object-Oriented Bayesian Network is modeled to reason about the lane on which the ego-vehicle is driving on using the information from these two input systems. The probabilistic approach is suitable because of the uncertain and inaccurate nature of the sensorial information. Due to the need of lateral painted landmarks, the method is dedicated to the segment of roads linked to an intersection.