A Review of the Bayesian Occupancy Filter
AbstractAutonomous 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. View Full-Text
Share & Cite This Article
Saval-Calvo, M.; Medina-Valdés, L.; Castillo-Secilla, J.M.; Cuenca-Asensi, S.; Martínez-Álvarez, A.; Villagrá, J. A Review of the Bayesian Occupancy Filter. Sensors 2017, 17, 344.
Saval-Calvo M, Medina-Valdés L, Castillo-Secilla JM, Cuenca-Asensi S, Martínez-Álvarez A, Villagrá J. A Review of the Bayesian Occupancy Filter. Sensors. 2017; 17(2):344.Chicago/Turabian Style
Saval-Calvo, Marcelo; Medina-Valdés, Luis; Castillo-Secilla, José M.; Cuenca-Asensi, Sergio; Martínez-Álvarez, Antonio; Villagrá, Jorge. 2017. "A Review of the Bayesian Occupancy Filter." Sensors 17, no. 2: 344.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.