{"id":"https://openalex.org/W2736020201","doi":"https://doi.org/10.1109/ijcnn.2017.7965995","title":"Predicted-occupancy grids for vehicle safety applications based on autoencoders and the Random Forest algorithm","display_name":"Predicted-occupancy grids for vehicle safety applications based on autoencoders and the Random Forest algorithm","publication_year":2017,"publication_date":"2017-05-01","ids":{"openalex":"https://openalex.org/W2736020201","doi":"https://doi.org/10.1109/ijcnn.2017.7965995","mag":"2736020201"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2017.7965995","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2017.7965995","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2512.12901","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5060156076","display_name":"Parthasarathy Nadarajan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210106192","display_name":"Technische Hochschule Ingolstadt","ror":"https://ror.org/02bxzcy64","country_code":"DE","type":"education","lineage":["https://openalex.org/I4210106192"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Parthasarathy Nadarajan","raw_affiliation_strings":["Technische Hochschule Ingolstadt, Ingolstadt, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technische Hochschule Ingolstadt, Ingolstadt, Germany","institution_ids":["https://openalex.org/I4210106192"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058811339","display_name":"Michael Botsch","orcid":"https://orcid.org/0000-0002-0900-1697"},"institutions":[{"id":"https://openalex.org/I4210106192","display_name":"Technische Hochschule Ingolstadt","ror":"https://ror.org/02bxzcy64","country_code":"DE","type":"education","lineage":["https://openalex.org/I4210106192"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Michael Botsch","raw_affiliation_strings":["Technische Hochschule Ingolstadt, Ingolstadt, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technische Hochschule Ingolstadt, Ingolstadt, Germany","institution_ids":["https://openalex.org/I4210106192"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045872670","display_name":"Sebastian Sardi\u00f1a","orcid":"https://orcid.org/0000-0003-2962-0118"},"institutions":[{"id":"https://openalex.org/I82951845","display_name":"RMIT University","ror":"https://ror.org/04ttjf776","country_code":"AU","type":"education","lineage":["https://openalex.org/I82951845"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Sebastian Sardina","raw_affiliation_strings":["RMIT University, Melbourne, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RMIT University, Melbourne, Australia","institution_ids":["https://openalex.org/I82951845"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9061,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.76375186,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1244","last_page":"1251"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9958999752998352,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9861000180244446,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.7104764580726624},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6988589763641357},{"id":"https://openalex.org/keywords/occupancy-grid-mapping","display_name":"Occupancy grid mapping","score":0.6842300295829773},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5998839139938354},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5925284624099731},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5287394523620605},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.5268717408180237},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5226680636405945},{"id":"https://openalex.org/keywords/occupancy","display_name":"Occupancy","score":0.49979496002197266},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47544264793395996},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38535434007644653},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3221897482872009},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2193765640258789},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.17960965633392334},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09945955872535706},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.08593609929084778}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7104764580726624},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6988589763641357},{"id":"https://openalex.org/C57077369","wikidata":"https://www.wikidata.org/wiki/Q7075747","display_name":"Occupancy grid mapping","level":4,"score":0.6842300295829773},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5998839139938354},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5925284624099731},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5287394523620605},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.5268717408180237},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5226680636405945},{"id":"https://openalex.org/C160331591","wikidata":"https://www.wikidata.org/wiki/Q7075743","display_name":"Occupancy","level":2,"score":0.49979496002197266},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47544264793395996},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38535434007644653},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3221897482872009},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2193765640258789},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.17960965633392334},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09945955872535706},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.08593609929084778},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/ijcnn.2017.7965995","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2017.7965995","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2512.12901","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.12901","pdf_url":"https://arxiv.org/pdf/2512.12901","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:alma.61RMIT_INST:11248035820001341","is_oa":false,"landing_page_url":"https://doi.org/10.1109/IJCNN.2017.7965995","pdf_url":null,"source":{"id":"https://openalex.org/S4306402074","display_name":"RMIT Research Repository (RMIT University Library)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I82951845","host_organization_name":"RMIT University","host_organization_lineage":["https://openalex.org/I82951845"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"pmh:oai:figshare.com:article/27401289","is_oa":true,"landing_page_url":"https://figshare.com/articles/conference_contribution/Predicted-occupancy_grids_for_vehicle_safety_applications_based_on_autoencoders_and_the_random_forest_algorithm/27401289","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference contribution"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2512.12901","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.12901","pdf_url":"https://arxiv.org/pdf/2512.12901","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320324887","display_name":"Shandong Academy of Sciences","ror":"https://ror.org/04y8d6y55"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2736020201.pdf","grobid_xml":"https://content.openalex.org/works/W2736020201.grobid-xml"},"referenced_works_count":22,"referenced_works":["https://openalex.org/W1869778509","https://openalex.org/W2025768430","https://openalex.org/W2058079053","https://openalex.org/W2069230091","https://openalex.org/W2100495367","https://openalex.org/W2101125181","https://openalex.org/W2120096422","https://openalex.org/W2131762276","https://openalex.org/W2135215819","https://openalex.org/W2135833910","https://openalex.org/W2158164339","https://openalex.org/W2166813035","https://openalex.org/W2187281534","https://openalex.org/W2290801524","https://openalex.org/W2418020448","https://openalex.org/W2494873165","https://openalex.org/W2514443522","https://openalex.org/W2548160836","https://openalex.org/W2755472872","https://openalex.org/W2911964244","https://openalex.org/W6639086533","https://openalex.org/W6696300133"],"related_works":["https://openalex.org/W4282043467","https://openalex.org/W3013693939","https://openalex.org/W2162255319","https://openalex.org/W3033776171","https://openalex.org/W5037887","https://openalex.org/W1999050017","https://openalex.org/W4293877624","https://openalex.org/W4229444815","https://openalex.org/W2889302474","https://openalex.org/W4321789545"],"abstract_inverted_index":{"In":[0,127],"this":[1],"paper,":[2],"a":[3,18,34,39,42,84,112,150],"probabilistic":[4,96],"space-time":[5,97],"representation":[6,19,108],"of":[7,53,64,73,86,104,134,158,165,181,187],"complex":[8,35],"traffic":[9,36,54,71,82,105,188],"scenarios":[10,189],"is":[11,20,46,58,90,109,121,144,169,195],"predicted":[12],"using":[13,149],"machine":[14,161],"learning":[15,132,162],"algorithms.":[16],"Such":[17],"significant":[21],"for":[22,60],"all":[23],"active":[24],"vehicle":[25],"safety":[26],"applications":[27],"especially":[28],"when":[29],"performing":[30],"dynamic":[31],"maneuvers":[32],"in":[33,171,174],"scenario.":[37],"As":[38],"first":[40],"step,":[41],"hierarchical":[43],"situation":[44],"classifier":[45,57],"used":[47],"to":[48,93,118,129,138,146,183,191],"distinguish":[49],"the":[50,62,65,69,74,95,101,119,131,135,142,159,185],"different":[51],"types":[52],"scenarios.":[55],"This":[56,107],"responsible":[59],"identifying":[61],"type":[63],"road":[66],"infrastructure":[67],"and":[68,137,167,173,190],"safety-relevant":[70],"participants":[72],"driving":[75],"environment.":[76],"With":[77],"each":[78],"class":[79],"representing":[80],"similar":[81],"scenarios,":[83],"set":[85],"Random":[87],"Forests":[88],"(RFs)":[89],"individually":[91],"trained":[92],"predict":[94],"representation,":[98],"which":[99],"depicts":[100],"future":[102],"behavior":[103],"participants.":[106],"termed":[110],"as":[111],"Predicted-Occupancy":[113],"Grid":[114,125],"(POG).":[115],"The":[116,155],"input":[117],"RFs":[120,136,168],"an":[122],"Augmented":[123],"Occupancy":[124],"(AOG).":[126],"order":[128],"increase":[130],"accuracy":[133],"perform":[139],"better":[140],"predictions,":[141],"AOG":[143],"reduced":[145],"low-dimensional":[147],"features":[148],"Stacked":[151],"Denoising":[152],"Autoencoder":[153],"(SDA).":[154],"excellent":[156],"performance":[157],"proposed":[160],"approach":[163],"consisting":[164],"SDAs":[166],"demonstrated":[170],"simulations":[172],"experiments":[175],"with":[176],"real":[177],"vehicles.":[178],"An":[179],"application":[180],"POGs":[182],"estimate":[184],"criticality":[186],"determine":[192],"safe":[193],"trajectories":[194],"also":[196],"presented.":[197]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-03T08:13:44.112507","created_date":"2025-10-10T00:00:00"}
