Curb detection for driving assistance systems: A cubic spline-based approach

Abstract

In this paper we present a real-time algorithm that detects curbs using a cubic spline model. A Digital Elevation Map (DEM) is used to represent the dense stereovision data. Curb measurements (cells) are detected on the current frame DEM. In order to compensate the small number of curb measurements for each frame we perform temporal integration. The result is a rich set of curb measurements that provides a good support for the least square cubic spline fitting. Thus, the curb cubic spline approximation is more stable and available on a much larger area, around the ego car. This compensates the limited field of view of typical stereo sensors. The detected curbs enrich the description of the ego car's surrounding 3D environment and can be used for driving assistance applications.