Abstract
Every year, backing-up maneuvers are responsible for hundreds of accidents in the world. There is lots of R&D to find systems that can avoid these accidents. Vision-based obstacle sensors are promising systems because they have ability to understand the outdoor environment. But they have difficulties with lighting conditions such as strong shadows and highlights. This paper presents a real-time wide-angle stereovision system that monitors a 6m area at the back of the vehicle in typical urban driving. Detection is achieved by extracting both obstacles and road surfaces. The main challenge is to recover the complete road surface when stereo fails, mainly with difficult lighting conditions: dynamic change of lighting conditions, shadows, highlights… Marking ground regions as unknown where 3D stereo information is missing will stop the road surface extraction and create false alarms. The contribution of this paper is an algorithm that repairs missing information of the stereo disparity map to have dense information of the scene. We show detection results under difficult lighting conditions. Road surface is correctly marked and obstacles are detected up to 6 meters.
Keywords: stereovision, shadow, highlight, homogeneous obstacle, free-space