{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T23:27:34Z","timestamp":1780356454611,"version":"3.54.1"},"reference-count":44,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2017,2,10]],"date-time":"2017-02-10T00:00:00Z","timestamp":1486684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Autonomous vehicle systems are currently the object of intense research within scienti\ufb01c 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 \ufb01ve 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.<\/jats:p>","DOI":"10.3390\/s17020344","type":"journal-article","created":{"date-parts":[[2017,2,15]],"date-time":"2017-02-15T10:09:07Z","timestamp":1487153347000},"page":"344","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["A Review of the Bayesian Occupancy Filter"],"prefix":"10.3390","volume":"17","author":[{"given":"Marcelo","family":"Saval-Calvo","sequence":"first","affiliation":[{"name":"University Institute for Computing Research, University of Alicante, 03690 San Vicente del Raspeig, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luis","family":"Medina-Vald\u00e9s","sequence":"additional","affiliation":[{"name":"University Institute for Computing Research, University of Alicante, 03690 San Vicente del Raspeig, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jos\u00e9","family":"Castillo-Secilla","sequence":"additional","affiliation":[{"name":"University Institute for Computing Research, University of Alicante, 03690 San Vicente del Raspeig, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sergio","family":"Cuenca-Asensi","sequence":"additional","affiliation":[{"name":"University Institute for Computing Research, University of Alicante, 03690 San Vicente del Raspeig, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio","family":"Mart\u00ednez-\u00c1lvarez","sequence":"additional","affiliation":[{"name":"University Institute for Computing Research, University of Alicante, 03690 San Vicente del Raspeig, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3963-7952","authenticated-orcid":false,"given":"Jorge","family":"Villagr\u00e1","sequence":"additional","affiliation":[{"name":"Centre for Automation and Robotics (UPM-CSIC), 28500 Arganda del Rey, Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,2,10]]},"reference":[{"key":"ref_1","first-page":"61","article-title":"Sensor Fusion in Certainty Grids for Mobile Robots","volume":"9","author":"Moravec","year":"1988","journal-title":"AI Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/2.30720","article-title":"Using occupancy grids for mobile robot perception and navigation","volume":"22","author":"Elfes","year":"1989","journal-title":"Computer"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Azor\u00edn-L\u00f3pez, J., Saval-Calvo, M., Fuster-Guill\u00f3, A., and Garc\u00eda-Rodr\u00edguez, J. (2013, January 4\u20139). Human Behaviour Recognition based on Trajectory Analysis using Neural Networks. Proceedings of the 2013 International Joint Conference on Neural Networks (IJCNN), Dallas, TX, USA.","DOI":"10.1109\/IJCNN.2013.6706724"},{"key":"ref_4","unstructured":"Coue, C., Fraichard, T., Bessiere, P., and Mazer, E. (2003, January 14\u201319). Using Bayesian Programming for multi-sensor multi-target tracking in automotive applications. Proceedings of the 2003 IEEE International Conference on Robotics and Automation (Cat. No. 03CH37422), Taipei, Taiwan."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kurdej, M., Moras, J., Cherfaoui, V., and Bonnifait, P. (2012). Map-Aided Fusion Using Evidential Grids for Mobile Perception in Urban Environment, Springer.","DOI":"10.1007\/978-3-642-29461-7_40"},{"key":"ref_6","unstructured":"Moras, J., Cherfaoui, V., and Bonnifait, P. (2014, January 7\u201310). Evidential grids information management in dynamic environments. Proceedings of the 2014 17th International Conference on Information Fusion (FUSION), Salamanca, Spain."},{"key":"ref_7","unstructured":"Cou\u00e9, C. (2003). Mod\u00e8le Bay\u00e9sien Pour L\u2019analyse Multimodale D\u2019environnementsdynamiques et Encombr\u00e9s: Application \u00e0 L\u2019assistance \u00e0 la Conduite en Milieu Urbain. [Ph.D. Thesis, Institut National Polytechnique de Grenoble-INPG]."},{"key":"ref_8","unstructured":"Coue, C., Fraichard, T., Bessiere, P., and Mazer, E. (October, January 30). Multi-sensor data fusion using Bayesian programming: An automotive application. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and System, Lausanne, Switzerland."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1177\/0278364906061158","article-title":"Bayesian Occupancy Filtering for Multitarget Tracking: An Automotive Application","volume":"25","author":"Pradalier","year":"2006","journal-title":"Int. J. Robot. Res."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Danescu, R., Oniga, F., and Nedevschi, S. (2010, January 21\u201324). Particle grid tracking system for stereovision based environment perception. Proceedings of the 2010 IEEE Intelligent Vehicles Symposium, La Jolla, CA, USA.","DOI":"10.1109\/IVS.2010.5548076"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1331","DOI":"10.1109\/TITS.2011.2158097","article-title":"Modeling and Tracking the Driving Environment With a Particle-Based Occupancy Grid","volume":"12","author":"Danescu","year":"2011","journal-title":"IEEE Trans. Intell. Trans. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2929","DOI":"10.3390\/s130302929","article-title":"Dynamic Obstacle Avoidance Using Bayesian Occupancy Filter and Approximate Inference","volume":"13","author":"Llamazares","year":"2013","journal-title":"Sensors"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Negre, A., Rummelhard, L., and Laugier, C. (2014, January 8\u201311). Hybrid sampling Bayesian Occupancy Filter. Proceedings of the 2014 IEEE Intelligent Vehicles Symposium, Ypsilanti, MI, USA.","DOI":"10.1109\/IVS.2014.6856554"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Chen, C., Tay, C., Laugier, C., and Mekhnacha, K. (2006, January 5\u20138). Dynamic Environment Modeling with Gridmap: A Multiple-Object Tracking Application. Proceedings of the 2006 9th International Conference on Control, Automation, Robotics and Vision, Grand Hyatt, Singapore.","DOI":"10.1109\/ICARCV.2006.345399"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1504\/IJVAS.2008.016483","article-title":"An efficient formulation of the Bayesian occupation filter for target tracking in dynamic environments","volume":"6","author":"Tay","year":"2008","journal-title":"Int. J. Veh. Auton. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Gindele, T., Brechtel, S., Schroder, J., and Dillmann, R. (2009, January 3\u20135). Bayesian Occupancy grid Filter for dynamic environments using prior map knowledge. Proceedings of the 2009 IEEE Intelligent Vehicles Symposium, Xi\u2019an, China.","DOI":"10.1109\/IVS.2009.5164357"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Brechtel, S., Gindele, T., and Dillmann, R. (2010, January 3\u20138). Recursive importance sampling for efficient grid-based occupancy filtering in dynamic environments. Proceedings of the 2010 IEEE International Conference on Robotics and Automation, Anchorage, AK, USA.","DOI":"10.1109\/ROBOT.2010.5509931"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Baig, Q., Perrollaz, M., Nascimento, J.B.D., and Laugier, C. (2012, January 5\u20137). Using fast classification of static and dynamic environment for improving Bayesian occupancy filter (BOF) and tracking. Proceedings of the 2012 12th International Conference on Control Automation Robotics & Vision (ICARCV), Guangzhou, China.","DOI":"10.1109\/ICARCV.2012.6485235"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/MITS.2011.942779","article-title":"Probabilistic Analysis of Dynamic Scenes and Collision Risks Assessment to Improve Driving Safety","volume":"3","author":"Laugier","year":"2011","journal-title":"IEEE Intell. Trans. Syst. Mag."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Baig, Q., Perrollaz, M., Botelho, J., and Laugier, C. (2012, January 7). Fast classification of static and dynamic environment for Bayesian Occupancy Filter (BOF). Proceedings of the IROS12 4th Workshop on Planning, Perception and Navigation for Intelligent Vehicles, Vilamoura, Portugal.","DOI":"10.1109\/ICARCV.2012.6485235"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1109\/MRA.2013.2297812","article-title":"A Robust Motion Detection Technique for Dynamic Environment Monitoring: A Framework for Grid-Based Monitoring of the Dynamic Environment","volume":"21","author":"Baig","year":"2014","journal-title":"IEEE Robotics Autom. Mag."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1109\/TPAMI.2007.1174","article-title":"Multicamera People Tracking with a Probabilistic Occupancy Map","volume":"30","author":"Fleuret","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_23","unstructured":"Fulgenzi, C. (2009). Autonomous Navigation in Dynamic Uncertain Environment Using Probabilistic Models of Perception and Collision Risk Prediction. [Ph.D. Thesis, Institut National Polytechnique de Grenoble]."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Fulgenzi, C., Spalanzani, A., and Laugier, C. (2007, January 10\u201314). Dynamic Obstacle Avoidance in uncertain environment combining PVOs and Occupancy Grid. Proceedings of the 2007 IEEE International Conference on Robotics and Automation, Roma, Italy.","DOI":"10.1109\/ROBOT.2007.363554"},{"key":"ref_25","unstructured":"Mekhnacha, K., and Raulo, D. (2008, January 29\u201330). Robust multi-target sensing\/tracking in the Bayesian Occupancy Filter framework. Proceedings of the Journees Francophone sur les Reseaux Bayesiens, Lyon, France."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ros, J., and Mekhnacha, K. (2009, January 5\u20137). Multi-sensor human tracking with the Bayesian Occupancy Filter. Proceedings of the 2009 16th International Conference on Digital Signal Processing, Santorini, Greece.","DOI":"10.1109\/ICDSP.2009.5201201"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ros, J., and Mekhnacha, K. (2010, January 26\u201329). A generative model for 3D range sensors in the Bayesian Occupancy filter framework: Application for fusion in smart home monitoring. Proceedings of the 2010 13th International Conference on Information Fusion, Edinburgh, UK.","DOI":"10.1109\/ICIF.2010.5712110"},{"key":"ref_28","unstructured":"Hsieh, A.M., Khatib, O., and Kumar, V. (2014, January 15\u201318). Probabilistic Grid-Based Collision Risk Prediction for Driving Application. Proceedings of the 14th International Symposium on Experimental Robotics, Marrakech and Essaouira, Morocco."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Bessi\u00e8re, P., Laugier, C., and Siegwart, R. (2008). Probabilistic Reasoning and Decision Making in Sensory-Motor Systems, Springer.","DOI":"10.1007\/978-3-540-79007-5"},{"key":"ref_30","unstructured":"Yguel, M., Tay, C., Mekhnacha, K., and Laugier, C. (2006). Velocity Estimation on the Bayesian Occupancy Filter for Multi-Target Tracking, INRIA. Technical Report; Grenoble-Rh\u00f4ne Alpes."},{"key":"ref_31","unstructured":"Mekhnacha, K., Mao, Y., Raulo, D., and Laugier, C. (2008, January 22\u201326). Bayesian occupancy filter based \u201cFast Clustering-Tracking\u201d algorithm. Proceedings of the IEEE\/RSJ IROS 2008 Workshop on Safe Navigation, Nice, Italy."},{"key":"ref_32","unstructured":"Mekhnacha, K., Mao, Y., Raulo, D., and Laugier, C. (2008). Multisensor Fusion and Integration for Intelligent Systems, Springer."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Nuss, D., Yuan, T., Krehl, G., Stuebler, M., Reuter, S., and Dietmayer, K. (July, January 28). Fusion of laser and radar sensor data with a sequential Monte Carlo Bayesian occupancy filter. Proceedings of the 2015 IEEE Intelligent Vehicles Symposium (IV), Seoul, Korea.","DOI":"10.1109\/IVS.2015.7225827"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Oh, S.I., and Kang, H.B. (2015, January 7\u201313). A Modified Sequential Monte Carlo Bayesian Occupancy Filter Using Linear Opinion Pool for Grid Mapping. Proceedings of the 2015 IEEE International Conference on Computer Vision Workshop (ICCVW), Santiago, Chile.","DOI":"10.1109\/ICCVW.2015.34"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Adarve, J.D., Perrollaz, M., Makris, A., and Laugier, C. (2012, January 14\u201318). Computing occupancy grids from multiple sensors using linear opinion pools. Proceedings of the 2012 IEEE International Conference on Robotics and Automation, St. Paul, MN, USA.","DOI":"10.1109\/ICRA.2012.6224976"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Perrollaz, M., Yoder, J.D., Spalanzani, A., and Laugier, C. (2010, January 18\u201322). Using the disparity space to compute occupancy grids from stereo-vision. Proceedings of the 2010 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan.","DOI":"10.1109\/IROS.2010.5649690"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Perrollaz, M., Spalanzani, A., and Aubert, D. (2010, January 21\u201324). Probabilistic representation of the uncertainty of stereo-vision and application to obstacle detection. Proceedings of the 2010 IEEE Intelligent Vehicles Symposium, La Jolla, CA, USA.","DOI":"10.1109\/IVS.2010.5548010"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Perrollaz, M., Yoder, J.D., and Laugier, C. (2010, January 19\u201322). Using obstacles and road pixels in the disparity-space computation of stereo-vision based occupancy grids. Proceedings of the 13th International IEEE Conference on Intelligent Transportation Systems, Madeira Island, Portugal.","DOI":"10.1109\/ITSC.2010.5625162"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.1109\/TITS.2012.2188393","article-title":"A Visibility-Based Approach for Occupancy Grid Computation in Disparity Space","volume":"13","author":"Perrollaz","year":"2012","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Yoder, J.D., Perrollaz, M., Paromtchik, I.E., Mao, Y., and Laugier, C. (2014). Experiments in Vision-Laser Fusion Using the Bayesian Occupancy Filter, Springer.","DOI":"10.1007\/978-3-642-28572-1_62"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1504\/IJVAS.2008.016478","article-title":"Efficient GPU-based construction of occupancy grids using several laser range-finders","volume":"6","author":"Yguel","year":"2008","journal-title":"Int. J. Veh. Auton. Syst."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Rakotovao, T.A., Puschini, D.P., Mottin, J., Rummelhard, L., Negre, A., and Laugier, C. (2015, January 19\u201321). Intelligent Vehicle Perception: Toward the Integration on Embedded Many-core. Proceedings of the 6th Workshop on Parallel Programming and Run-Time Management Techniques for Many-Core Architectures (PARMA-DITAM\u201915), Amsterdam, The Netherlands.","DOI":"10.1145\/2701310.2701313"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Rakotovao, T., Mottin, J., Puschini, D., and Laugier, C. (2015, January 1\u20133). Real-time power-efficient integration of multi-sensor occupancy grid on many-core. Proceedings of the 2015 IEEE International Workshop on Advanced Robotics and Its Social Impacts (ARSO), Lyon, France.","DOI":"10.1109\/ARSO.2015.7428211"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Melpignano, D., Benini, L., Flamand, E., Jego, B., Lepley, T., Haugou, G., Clermidy, F., and Dutoit, D. (2012, January 3\u20137). Platform 2012, a many-core computing accelerator for embedded SoCs. Proceedings of the 49th Annual Design Automation Conference on DAC\u201912, San Francisco, CA, USA.","DOI":"10.1145\/2228360.2228568"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/2\/344\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:28:00Z","timestamp":1760207280000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/2\/344"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,2,10]]},"references-count":44,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2017,2]]}},"alternative-id":["s17020344"],"URL":"https:\/\/doi.org\/10.3390\/s17020344","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,2,10]]}}}