m (Scipediacontent moved page Draft Content 159437040 to Collado et al 2006a) |
|||
Line 3: | Line 3: | ||
Proceeding of: 8th International Conference, ACIVS 2006, Antwerp, Belgium, September 18-21, 2006 This paper presents a Road Detection and Classification algorithm for Driver Assistance Systems (DAS), which tracks several road lanes and identifies the type of lane boundaries. The algorithm uses an edge filter to extract the longitudinal road markings to which a straight lane model is fitted. Next, the type of right and left lane boundaries (continuous, broken or merge line) is identified using a Fourier analysis. Adjacent lanes are searched when broken or merge lines are detected. Although the knowledge of the line type is essential for a robust DAS, it has been seldom considered in previous works. This knowledge helps to guide the search for other lanes, and it is the basis to identify the type of road (one-way, two-way or freeway), as well as to tell the difference between allowed and forbidden maneuvers, such as crossing a continuous line. Publicado | Proceeding of: 8th International Conference, ACIVS 2006, Antwerp, Belgium, September 18-21, 2006 This paper presents a Road Detection and Classification algorithm for Driver Assistance Systems (DAS), which tracks several road lanes and identifies the type of lane boundaries. The algorithm uses an edge filter to extract the longitudinal road markings to which a straight lane model is fitted. Next, the type of right and left lane boundaries (continuous, broken or merge line) is identified using a Fourier analysis. Adjacent lanes are searched when broken or merge lines are detected. Although the knowledge of the line type is essential for a robust DAS, it has been seldom considered in previous works. This knowledge helps to guide the search for other lanes, and it is the basis to identify the type of road (one-way, two-way or freeway), as well as to tell the difference between allowed and forbidden maneuvers, such as crossing a continuous line. Publicado | ||
− | |||
− | |||
− | |||
− | |||
− | |||
Line 18: | Line 13: | ||
* [https://e-archivo.uc3m.es/bitstream/10016/7108/3/adaptative_collado_ACIVS_2006_ps.pdf https://e-archivo.uc3m.es/bitstream/10016/7108/3/adaptative_collado_ACIVS_2006_ps.pdf] | * [https://e-archivo.uc3m.es/bitstream/10016/7108/3/adaptative_collado_ACIVS_2006_ps.pdf https://e-archivo.uc3m.es/bitstream/10016/7108/3/adaptative_collado_ACIVS_2006_ps.pdf] | ||
− | * [http://link.springer.com/content/pdf/10.1007/11864349_105 http://link.springer.com/content/pdf/10.1007/11864349_105],[http://dx.doi.org/10.1007/11864349_105 http://dx.doi.org/10.1007/11864349_105] | + | * [http://link.springer.com/content/pdf/10.1007/11864349_105 http://link.springer.com/content/pdf/10.1007/11864349_105], |
+ | : [http://dx.doi.org/10.1007/11864349_105 http://dx.doi.org/10.1007/11864349_105] | ||
− | * [https://link.springer.com/chapter/10.1007 | + | * [https://dblp.uni-trier.de/db/conf/acivs/acivs2006.html#ColladoHEA06 https://dblp.uni-trier.de/db/conf/acivs/acivs2006.html#ColladoHEA06], |
+ | : [https://link.springer.com/chapter/10.1007%2F11864349_105 https://link.springer.com/chapter/10.1007%2F11864349_105], | ||
+ | : [http://core.ac.uk/display/29400753 http://core.ac.uk/display/29400753], | ||
+ | : [http://portal.uc3m.es/portal/page/portal/dpto_ing_sistemas_automatica/investigacion/lab_sist_inteligentes_old/miembros/jose_maria_armingol/acivs06.pdf http://portal.uc3m.es/portal/page/portal/dpto_ing_sistemas_automatica/investigacion/lab_sist_inteligentes_old/miembros/jose_maria_armingol/acivs06.pdf], | ||
+ | : [https://dl.acm.org/citation.cfm?id=2092641 https://dl.acm.org/citation.cfm?id=2092641], | ||
+ | : [https://doi.org/10.1007/11864349_105 https://doi.org/10.1007/11864349_105], | ||
+ | : [https://rd.springer.com/chapter/10.1007/11864349_105 https://rd.springer.com/chapter/10.1007/11864349_105], | ||
+ | : [https://academic.microsoft.com/#/detail/1631218178 https://academic.microsoft.com/#/detail/1631218178] | ||
* [ ] | * [ ] |
Proceeding of: 8th International Conference, ACIVS 2006, Antwerp, Belgium, September 18-21, 2006 This paper presents a Road Detection and Classification algorithm for Driver Assistance Systems (DAS), which tracks several road lanes and identifies the type of lane boundaries. The algorithm uses an edge filter to extract the longitudinal road markings to which a straight lane model is fitted. Next, the type of right and left lane boundaries (continuous, broken or merge line) is identified using a Fourier analysis. Adjacent lanes are searched when broken or merge lines are detected. Although the knowledge of the line type is essential for a robust DAS, it has been seldom considered in previous works. This knowledge helps to guide the search for other lanes, and it is the basis to identify the type of road (one-way, two-way or freeway), as well as to tell the difference between allowed and forbidden maneuvers, such as crossing a continuous line. Publicado
The different versions of the original document can be found in:
Published on 01/01/2006
Volume 2006, 2006
DOI: 10.1007/11864349_105
Licence: CC BY-NC-SA license
Are you one of the authors of this document?