Kalman filter approach for lane extraction and following

In this paper, we focus on the use of Kalman filter approach to lane extraction and following. We assume a structured environment where a mobile vehicle equipped with a camera sensor, situated at a fixed distance from the vehicle frame, is navigating. A quadratic model of the road is considered. This enables the state vector of the filter to be coincided with the three parameters pertaining to a second-order polynom. The determination of the state model is carried out considering either a pure translation of the (i+1)th frame with respect to the ith frame attached respectively to the images at time t(i) and t(i+1), or a combination of both translation and rotation. While the measurement model comes down to the camera images obtained using the inverse perspective transformation, which due to the road model, permits a straightforward link between the x-y co-ordinates of images and the state vector parameters of the road. The performance of these two state models are compared with a purely measurement approach where least squares methodology is performed regardless the state model. The estimates of the filter are used by the vehicle in order to update its own knowledge about the environment and to accomplish the task of "road following". Furthermore, this permits the vision system to encompass into a more general structure of Integrated Supervisory Control Systems (ISCS), where multiple functionalities are allowed.