A Review of the Bayesian Occupancy Filter
Marcelo Saval-Calvo 1, Luis Medina-Valdés 1, José María Castillo-Secilla 1,*, Sergio Cuenca-Asensi 1, Antonio Martínez-Álvarez 1 and Jorge Villagrá 2
1
University Institute for Computing Research, University of Alicante, 03690 San Vicente del Raspeig, Spain
2
Centre for Automation and Robotics (UPM-CSIC), 28500 Arganda del Rey, Madrid, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Felipe Jimenez
Sensors 2017, 17(2), 344; https://doi.org/10.3390/s17020344
Received: 29 October 2016 / Revised: 25 January 2017 / Accepted: 3 February 2017 / Published: 10 February 2017
(This article belongs to the Special Issue Sensors for Autonomous Road Vehicles)
Abstract
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. View Full-Text
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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.
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