A Hybrid Vision-Map Method for Urban Road Detection
| Autor: | Carlos Fernández, David Fernández-Llorca, Miguel A. Sotelo |
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| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: | |
| Zdroj: | Journal of Advanced Transportation, Vol 2017 (2017) |
| Druh dokumentu: | article |
| ISSN: | 0197-6729 2042-3195 |
| DOI: | 10.1155/2017/7090549 |
| Popis: | A hybrid vision-map system is presented to solve the road detection problem in urban scenarios. The standardized use of machine learning techniques in classification problems has been merged with digital navigation map information to increase system robustness. The objective of this paper is to create a new environment perception method to detect the road in urban environments, fusing stereo vision with digital maps by detecting road appearance and road limits such as lane markings or curbs. Deep learning approaches make the system hard-coupled to the training set. Even though our approach is based on machine learning techniques, the features are calculated from different sources (GPS, map, curbs, etc.), making our system less dependent on the training set. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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