A Review of the Bayesian Occupancy Filter

Saval Calvo, Marcelo and Medina Valdés, Luis and Castillo Secilla, José María and Cuenca-Asensi, Sergio and Martínez-Álvarez, Antonio and Villagrá Serrano, Jorge (2017). A Review of the Bayesian Occupancy Filter. "Sensors", v. 17 (n. 2); pp. 1-11. ISSN 1424-8220. https://doi.org/10.3390/s17020344.

Description

Title: A Review of the Bayesian Occupancy Filter
Author/s:
  • Saval Calvo, Marcelo
  • Medina Valdés, Luis
  • Castillo Secilla, José María
  • Cuenca-Asensi, Sergio
  • Martínez-Álvarez, Antonio
  • Villagrá Serrano, Jorge
Item Type: Article
Título de Revista/Publicación: Sensors
Date: 2017
ISSN: 1424-8220
Volume: 17
Subjects:
Faculty: Centro de Automática y Robótica (CAR) UPM-CSIC
Department: Otro
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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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 abstract level of risk assessment. In addition, we present a study of implemented use cases to provide a practical understanding on the main uses of the BOF and its taxonomy.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainRTC-2015-3942-4TCAP-AUTOUnspecifiedRetos Colaboración 2014

More information

Item ID: 52856
DC Identifier: https://oa.upm.es/52856/
OAI Identifier: oai:oa.upm.es:52856
DOI: 10.3390/s17020344
Official URL: https://www.mdpi.com/1424-8220/17/2/344
Deposited by: Memoria Investigacion
Deposited on: 26 Aug 2022 07:11
Last Modified: 26 Aug 2022 07:11
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