Recently, cars are equipped with a lot of sensors for safety driving. We have been trying to store the driving-scene video with such sensor data and to detect the change of scenery of streets. Detection results can be used for building historical database of town scenery, automatic landmark updating of maps, and so forth. In order to compare images to detect changes, image retrieval taken at nearly identical locations is required as the first step. Since Global Positioning System (GPS) data essentially contain some noises, we cannot rely only on GPS data for our image retrieval. Therefore, we have developed an image retrieval algorithm employing edge-histogram-based image features in conjunction with hierarchical search. By using edge histograms projected onto the vertical and horizontal axes, the retrieval has been made robust to image variation due to weather change, clouds, obstacles, and so on. In addition, matching cost has been made small by limiting the matching candidates employing the hierarchical search. Experimental results have demonstrated that the mean retrieval accuracy has been improved from 65% to 76% for the front-view images and from 34% to 53% for the side-view images.