{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T09:55:52Z","timestamp":1777715752424,"version":"3.51.4"},"reference-count":21,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2000,5,1]],"date-time":"2000-05-01T00:00:00Z","timestamp":957139200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["The International Journal of Robotics Research"],"published-print":{"date-parts":[[2000,5]]},"abstract":"<jats:p>In this article, we present a method of nonmarked road following that is based on images coming from an onboard monochromatic camera. The principle is based first on a segmentation stage that makes it possible to locate the road area in the image, managing, if possible, the shadows on the roadway. The method is original since the algorithm must be running day as well as night (infrared camera) so it does not use color images. Furthermore, a single constant threshold is used whatever the analyzed sequence. Then, a localization stage estimates the vehicle\u2019s location on the roadway. The estimate of the parameters L (road width) and \u03b1 (camera inclination angle) (assumed known and constant for certain existing approaches) ensures a greater robustness of the other estimated parameters. Finally, a filtering stage is applied onto the previous data and predicts the position of the vehicle in the next image. Results are shown for each stage on both a normal nonmarked road and a forest lane sequence. The computational times are very low and will permit a real-time implementation on an experimental vehicle.<\/jats:p>","DOI":"10.1177\/02783640022066941","type":"journal-article","created":{"date-parts":[[2004,12,17]],"date-time":"2004-12-17T20:23:10Z","timestamp":1103314990000},"page":"411-423","source":"Crossref","is-referenced-by-count":5,"title":["A Dynamic Vision Algorithm to Locate a Vehicle on a Nonstructured Road"],"prefix":"10.1177","volume":"19","author":[{"given":"R.","family":"Aufr\u00e8re","sequence":"first","affiliation":[{"name":"LASMEA, UMR 6602 CNRS, Universit\u00e9 Blaise Pascal, F63177                         AUBIERE Cedex, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"R.","family":"Chapuis","sequence":"additional","affiliation":[{"name":"LASMEA, UMR 6602 CNRS, Universit\u00e9 Blaise Pascal, F63177                         AUBIERE Cedex, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"F.","family":"Chausse","sequence":"additional","affiliation":[{"name":"LASMEA, UMR 6602 CNRS, Universit\u00e9 Blaise Pascal, F63177                         AUBIERE Cedex, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2000,5,1]]},"reference":[{"key":"atypb1","unstructured":"Broggi, A. 1995. An image reorganization procedure for automotive road following systems. International Conference on Image Processing. Washington, DC: IEEE Computer Society ."},{"key":"atypb2","unstructured":"Broggi, A., Bertozzi, M., Gregoretti, F., Passerone, F., Sanso\u00e8, C., and Reyneri, L. 1997. A dedicated image processor exploiting both spatial and instruction-level parallelism. CAMP\u201997 Computer Architectures for Machine Perception. Boston: IEEE Computer Society ."},{"key":"atypb3","doi-asserted-by":"publisher","DOI":"10.1177\/027836499501400601"},{"key":"atypb4","doi-asserted-by":"crossref","unstructured":"Denasi, S., Lanzone, C., Martinese, P., Pettiti, G., and Quagli, G. 1994. Real time system for road following and obstacle detection . Proceedings of SPIE. Vol. 2347 Machine Vision Applications, Architectures, and System Integration III, Boston, MA, pp. 70\u201379 .","DOI":"10.1117\/12.188761"},{"key":"atypb5","doi-asserted-by":"crossref","unstructured":"Dickmanns, E. D., Behringer, R., Brudigam, C., Dickmanns, D., Thomanek, F., and Holt, V. 1993. An all-transputer visual autobahnautopilot\/copilot. 4th ICCV Berlin Germany, pp. 608\u2013615.","DOI":"10.1109\/ICCV.1993.378155"},{"key":"atypb6","doi-asserted-by":"publisher","DOI":"10.1109\/34.121789"},{"key":"atypb7","unstructured":"Diebolt, F. 1996. Road markings recognition. ICIP96, Lausanne, Switzerland , p. 17P8-17P8."},{"key":"atypb8","unstructured":"Ekinci, M., and Thomas, B. 1996. Road junction recognition and turn-offs for autonomous road vehicle navigation. ICPR96, Vienne, Austria , p. C7A.2-C7A.2."},{"key":"atypb9","doi-asserted-by":"crossref","unstructured":"Ferruz, J., and Ollero, A. 1997. Autonomous mobile robot control in non-structured environments based on real-time video processing. IROS 97, Grenoble, France , pp. 725\u2013731.","DOI":"10.1109\/IROS.1997.655091"},{"key":"atypb10","doi-asserted-by":"crossref","unstructured":"Huang, C., and Gillies, D. 1995. 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