A Review of the Bayesian Occupancy Filter
| Autor: | Marcelo Saval-Calvo, Luis Medina-Valdés, José María Castillo-Secilla, Sergio Cuenca-Asensi, Antonio Martínez-Álvarez, Jorge Villagrá |
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| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: | |
| Zdroj: | Sensors, Vol 17, Iss 2, p 344 (2017) |
| Druh dokumentu: | article |
| ISSN: | 1424-8220 17020344 |
| DOI: | 10.3390/s17020344 |
| Popis: | Autonomous vehicle systems are currently the object of intense research within scientific and industrial communities; however, many problems remain to be solved. One of the most critical aspects addressed in both autonomous driving and robotics is environment perception, since it consists of the ability to understand the surroundings of the vehicle to estimate risks and make decisions on future movements. In recent years, the Bayesian Occupancy Filter (BOF) method has been developed to evaluate occupancy by tessellation of the environment. A review of the BOF and its variants is presented in this paper. Moreover, we propose a detailed taxonomy where the BOF is decomposed into five progressive layers, from the level closest to the sensor to the highest abstractlevelofriskassessment. Inaddition,wepresentastudyofimplementedusecasestoprovide a practical understanding on the main uses of the BOF and its taxonomy. |
| Databáze: | Directory of Open Access Journals |
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