Estimations of previewed road curvatures and vehicular motion by a vision-based data fusion scheme

In this study, a new framework of vision-based estimation is developed using some data fusion schemes to obtain previewed road curvatures and vehicular motion states based on the scene viewed from an in-vehicle camera. The previewed curvatures are necessary for the guidance of an automatically steering vehicle, and the desired vehicular motion variables, including lateral deviation, heading angle, yaw rate, and sideslip angle, are also required for proper control of the vehicular lateral motion via steering. In this framework, physical relationships of previewed curvatures among consecutive images, motion variables in terms of image features searched at various levels in the image plane, and dynamic correlation among vehicular motion variables are derived as bases of data fusion to enhance the accuracy of estimation. The vision-based measurement errors are analyzed to determine the fusion gains based on the technique of a Kalman filter such that the measurements from the image plane and predictions of physical models can be properly integrated to obtain reliable estimations. Off-line experimental works using real road scenes are performed to verify the whole framework for image sensing.