{"id":"https://openalex.org/W2514443522","doi":"https://doi.org/10.1109/ivs.2016.7535556","title":"Probability estimation for Predicted-Occupancy Grids in vehicle safety applications based on machine learning","display_name":"Probability estimation for Predicted-Occupancy Grids in vehicle safety applications based on machine learning","publication_year":2016,"publication_date":"2016-06-01","ids":{"openalex":"https://openalex.org/W2514443522","doi":"https://doi.org/10.1109/ivs.2016.7535556","mag":"2514443522"},"language":"en","primary_location":{"id":"doi:10.1109/ivs.2016.7535556","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2016.7535556","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2512.12896","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, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technische Hochschule Ingolstadt, Germany","institution_ids":["https://openalex.org/I4210106192"]}]},{"author_position":"last","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, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technische Hochschule Ingolstadt, Germany","institution_ids":["https://openalex.org/I4210106192"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210106192"],"apc_list":null,"apc_paid":null,"fwci":1.0236,"has_fulltext":true,"cited_by_count":9,"citation_normalized_percentile":{"value":0.8085086,"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":"1285","last_page":"1292"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9948999881744385,"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"}},"topics":[{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9948999881744385,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9876000285148621,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.98580002784729,"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/occupancy-grid-mapping","display_name":"Occupancy grid mapping","score":0.8542135953903198},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7673020362854004},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5888898372650146},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.5883044004440308},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5798496007919312},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.5663902759552002},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5544690489768982},{"id":"https://openalex.org/keywords/occupancy","display_name":"Occupancy","score":0.4899267554283142},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4674421548843384},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44603753089904785},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.20785164833068848},{"id":"https://openalex.org/keywords/mobile-robot","display_name":"Mobile robot","score":0.1506698727607727},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.14421674609184265},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.08785218000411987}],"concepts":[{"id":"https://openalex.org/C57077369","wikidata":"https://www.wikidata.org/wiki/Q7075747","display_name":"Occupancy grid mapping","level":4,"score":0.8542135953903198},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7673020362854004},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5888898372650146},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.5883044004440308},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5798496007919312},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.5663902759552002},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5544690489768982},{"id":"https://openalex.org/C160331591","wikidata":"https://www.wikidata.org/wiki/Q7075743","display_name":"Occupancy","level":2,"score":0.4899267554283142},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4674421548843384},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44603753089904785},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.20785164833068848},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.1506698727607727},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14421674609184265},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.08785218000411987},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ivs.2016.7535556","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2016.7535556","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2512.12896","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.12896","pdf_url":"https://arxiv.org/pdf/2512.12896","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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2512.12896","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.12896","pdf_url":"https://arxiv.org/pdf/2512.12896","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":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.4699999988079071}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W2514443522.pdf"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W98862427","https://openalex.org/W131069610","https://openalex.org/W1518969363","https://openalex.org/W1520813427","https://openalex.org/W1574275106","https://openalex.org/W1594031697","https://openalex.org/W1840338487","https://openalex.org/W1917451876","https://openalex.org/W1964565648","https://openalex.org/W2013753693","https://openalex.org/W2019550475","https://openalex.org/W2056132907","https://openalex.org/W2058570191","https://openalex.org/W2080803848","https://openalex.org/W2087050866","https://openalex.org/W2097545165","https://openalex.org/W2120096422","https://openalex.org/W2122773086","https://openalex.org/W2139391802","https://openalex.org/W2143324623","https://openalex.org/W2151992080","https://openalex.org/W2163415743","https://openalex.org/W2167293612","https://openalex.org/W2330820318","https://openalex.org/W2336416123","https://openalex.org/W2567397379","https://openalex.org/W2751012431","https://openalex.org/W2911964244","https://openalex.org/W4285719527","https://openalex.org/W6604049124","https://openalex.org/W6631209646","https://openalex.org/W6653795072","https://openalex.org/W6703161083","https://openalex.org/W6743421510","https://openalex.org/W6843735874"],"related_works":["https://openalex.org/W4282043467","https://openalex.org/W2105697914","https://openalex.org/W3093197249","https://openalex.org/W1968324288","https://openalex.org/W2162255319","https://openalex.org/W5037887","https://openalex.org/W1999050017","https://openalex.org/W4293877624","https://openalex.org/W4229444815","https://openalex.org/W1538887534"],"abstract_inverted_index":{"This":[0,45,124,145],"paper":[1,66],"presents":[2],"a":[3,10,67,81,108,114,127],"method":[4],"to":[5,25,72,92,143,172],"predict":[6],"the":[7,21,31,40,47,50,61,86,93,103,120,132,136,141,162,174,177,187],"evolution":[8],"of":[9,20,42,52,64,85,96,122,131,135,148,167,190,200],"complex":[11],"traffic":[12,43,53,101,137,168],"scenario":[13,22,88,138],"with":[14,107,176],"multiple":[15],"objects.":[16],"The":[17,155,180],"current":[18,133],"state":[19,134],"is":[23,33,70,117,159],"assumed":[24],"be":[26,56,215],"known":[27],"from":[28],"sensors":[29],"and":[30,139,184,211],"prediction":[32],"taking":[34],"into":[35],"account":[36],"various":[37],"hypotheses":[38],"about":[39],"behavior":[41,51],"participants.":[44],"way,":[46],"uncertainties":[48],"regarding":[49],"participants":[54],"can":[55,214],"modelled":[57],"in":[58,151,204],"detail.":[59],"In":[60],"first":[62],"part":[63],"this":[65,197],"model-based":[68,104,178],"approach":[69,105,116,158],"presented":[71],"compute":[73],"Predicted-Occupancy":[74],"Grids":[75],"(POG),":[76],"which":[77],"are":[78,170,182],"introduced":[79],"as":[80],"grid-based":[82,129],"probabilistic":[83],"representation":[84,130,146],"future":[87],"hypotheses.":[89],"However,":[90],"due":[91],"large":[94],"number":[95],"possible":[97],"trajectories":[98],"for":[99,119,192],"each":[100],"participant,":[102],"comes":[106],"very":[109],"high":[110],"computational":[111],"load.":[112],"Thus,":[113],"machine-learning":[115,157,175],"adopted":[118,156],"computation":[121,189],"POGs.":[123,144],"work":[125],"uses":[126],"novel":[128],"performs":[140],"mapping":[142],"consists":[147],"augmented":[149],"cells":[150],"an":[152],"occupancy":[153],"grid.":[154],"based":[160],"on":[161],"Random":[163],"Forest":[164],"algorithm.":[165],"Simulations":[166],"scenarios":[169],"performed":[171],"compare":[173],"approach.":[179],"results":[181],"promising":[183],"could":[185],"enable":[186],"real-time":[188],"POGs":[191],"vehicle":[193,205],"safety":[194,206],"applications.":[195],"With":[196],"detailed":[198],"modelling":[199],"uncertainties,":[201],"crucial":[202],"components":[203],"systems":[207],"like":[208],"criticality":[209],"estimation":[210],"trajectory":[212],"planning":[213],"improved.":[216]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-07-03T08:13:44.112507","created_date":"2025-10-10T00:00:00"}
