Correlation of images has been used less in vision-based navigation. In this paper we present a novel method to estimate the orientation of the vehicle relative to the roadway by using image sequence. A parallel correlation algorithm is used to detect the difference between the current view of the roadway with its next view, and the orientation of the vehicle can be estimated reliably in real time. In order to account for figure variation caused by 3-D dynamic environment and perspective effect exerted by the camera system, a weighted correlation method has been developed based on planar motion assumption and reprojection transformation. This method has been implemented on PIPE, a pipelined image processing system, and tested in our campus roadway in combination with optical flow method for the detecting of moving objects. The advantage of the method is that the motion parameters can be extracted reliably without prerequisite for image sequence and no special road model is needed, as it adapts itself to rather complicated situations with other objects sharing the same environment.