{"id":"https://openalex.org/W4220795836","doi":"https://doi.org/10.1109/lra.2022.3151613","title":"Occupancy Flow Fields for Motion Forecasting in Autonomous Driving","display_name":"Occupancy Flow Fields for Motion Forecasting in Autonomous Driving","publication_year":2022,"publication_date":"2022-02-14","ids":{"openalex":"https://openalex.org/W4220795836","doi":"https://doi.org/10.1109/lra.2022.3151613"},"language":"en","primary_location":{"id":"doi:10.1109/lra.2022.3151613","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lra.2022.3151613","pdf_url":null,"source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Robotics and Automation Letters","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2203.03875","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5023703380","display_name":"Reza Mahjourian","orcid":"https://orcid.org/0000-0002-4457-8395"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Reza Mahjourian","raw_affiliation_strings":["Waymo, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"Waymo, Mountain View, CA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061842716","display_name":"Jinkyu Kim","orcid":"https://orcid.org/0000-0001-6520-2074"},"institutions":[{"id":"https://openalex.org/I197347611","display_name":"Korea University","ror":"https://ror.org/047dqcg40","country_code":"KR","type":"education","lineage":["https://openalex.org/I197347611"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jinkyu Kim","raw_affiliation_strings":["Department of Computer Science and Engineering, Korea University, Seoul, Korea"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Korea University, Seoul, Korea","institution_ids":["https://openalex.org/I197347611"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079961895","display_name":"Yuning Chai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuning Chai","raw_affiliation_strings":["Waymo, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"Waymo, Mountain View, CA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110774377","display_name":"Mingxing Tan","orcid":null},"institutions":[{"id":"https://openalex.org/I1291425158","display_name":"Google (United States)","ror":"https://ror.org/00njsd438","country_code":"US","type":"company","lineage":["https://openalex.org/I1291425158","https://openalex.org/I4210128969"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mingxing Tan","raw_affiliation_strings":["Google Brain, Sunnyvale, CA, USA"],"affiliations":[{"raw_affiliation_string":"Google Brain, Sunnyvale, CA, USA","institution_ids":["https://openalex.org/I1291425158"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031227461","display_name":"Ben Sapp","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ben Sapp","raw_affiliation_strings":["Waymo, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"Waymo, Mountain View, CA, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081024054","display_name":"Dragomir Anguelov","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dragomir Anguelov","raw_affiliation_strings":["Waymo, Mountain View, CA, USA"],"affiliations":[{"raw_affiliation_string":"Waymo, Mountain View, CA, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5023703380"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.2678,"has_fulltext":false,"cited_by_count":69,"citation_normalized_percentile":{"value":0.96537299,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"7","issue":"2","first_page":"5639","last_page":"5646"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9993000030517578,"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":0.9993000030517578,"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":0.988099992275238,"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":0.9872999787330627,"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","display_name":"Occupancy","score":0.8786617517471313},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.5626404881477356},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.46569502353668213},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4188447892665863},{"id":"https://openalex.org/keywords/aeronautics","display_name":"Aeronautics","score":0.3625950813293457},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.35885605216026306},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.24203380942344666},{"id":"https://openalex.org/keywords/architectural-engineering","display_name":"Architectural engineering","score":0.11348292231559753},{"id":"https://openalex.org/keywords/mechanics","display_name":"Mechanics","score":0.10967504978179932},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.10447007417678833}],"concepts":[{"id":"https://openalex.org/C160331591","wikidata":"https://www.wikidata.org/wiki/Q7075743","display_name":"Occupancy","level":2,"score":0.8786617517471313},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.5626404881477356},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.46569502353668213},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4188447892665863},{"id":"https://openalex.org/C178802073","wikidata":"https://www.wikidata.org/wiki/Q8421","display_name":"Aeronautics","level":1,"score":0.3625950813293457},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35885605216026306},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.24203380942344666},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.11348292231559753},{"id":"https://openalex.org/C57879066","wikidata":"https://www.wikidata.org/wiki/Q41217","display_name":"Mechanics","level":1,"score":0.10967504978179932},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.10447007417678833}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/lra.2022.3151613","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lra.2022.3151613","pdf_url":null,"source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Robotics and Automation Letters","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2203.03875","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2203.03875","pdf_url":"https://arxiv.org/pdf/2203.03875","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:2203.03875","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2203.03875","pdf_url":"https://arxiv.org/pdf/2203.03875","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":[{"id":"https://openalex.org/G6006432325","display_name":null,"funder_award_id":"NRF-2021R1C1C1009608","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W1567193306","https://openalex.org/W2136891917","https://openalex.org/W2167052694","https://openalex.org/W2412782625","https://openalex.org/W2532516272","https://openalex.org/W2769282630","https://openalex.org/W2780861787","https://openalex.org/W2898900571","https://openalex.org/W2899302124","https://openalex.org/W2905173465","https://openalex.org/W2955189650","https://openalex.org/W2963759562","https://openalex.org/W2967177252","https://openalex.org/W2968296999","https://openalex.org/W2980087597","https://openalex.org/W3000520642","https://openalex.org/W3012478636","https://openalex.org/W3028769608","https://openalex.org/W3029177463","https://openalex.org/W3034971973","https://openalex.org/W3035671534","https://openalex.org/W3090166818","https://openalex.org/W3114753236","https://openalex.org/W3116651890","https://openalex.org/W3156216502","https://openalex.org/W3172477795","https://openalex.org/W3208318476","https://openalex.org/W4281685999","https://openalex.org/W6631190155","https://openalex.org/W6634211352","https://openalex.org/W6755864109","https://openalex.org/W6768870957","https://openalex.org/W6769043036","https://openalex.org/W6769377150","https://openalex.org/W6775168941"],"related_works":["https://openalex.org/W4282043467","https://openalex.org/W2105697914","https://openalex.org/W3093197249","https://openalex.org/W1968324288","https://openalex.org/W1540010871","https://openalex.org/W3023979140","https://openalex.org/W3177545769","https://openalex.org/W2904068067","https://openalex.org/W1565491139","https://openalex.org/W2202433167"],"abstract_inverted_index":{"We":[0,131,179],"propose":[1,103],"Occupancy":[2,110],"Flow":[3,111],"Fields,":[4],"a":[5,22,42,104,117,184],"new":[6,118],"representation":[7,20],"for":[8,68],"motion":[9,53,69,93,144],"forecasting":[10],"of":[11,33,51,62,84,116,135,156,177],"multiple":[12],"agents,":[13,159],"an":[14],"important":[15],"task":[16],"in":[17,54,167],"autonomous":[18,187],"driving.Our":[19],"is":[21],"spatio-temporal":[23],"grid":[24,27],"with":[25,113],"each":[26],"cell":[28,35],"containing":[29],"both":[30],"the":[31,34,47,52,63,81,96,114,126,133,154,168,175,191],"probability":[32],"being":[36],"occupied":[37],"by":[38,173],"any":[39],"agent,":[40],"and":[41,49,73,94,128,146,190,195],"two-dimensional":[43],"flow":[44,119,129],"vector":[45],"representing":[46],"direction":[48],"magnitude":[50],"that":[55,108,122,164,197],"cell.":[56],"Our":[57],"method":[58],"successfully":[59],"mitigates":[60],"shortcomings":[61],"two":[64],"most":[65],"commonly-used":[66],"representations":[67],"forecasting:":[70],"trajectory":[71],"sets":[72],"occupancy":[74,77,127,142],"grids.":[75],"Although":[76],"grids":[78],"efficiently":[79],"represent":[80],"probabilistic":[82],"location":[83],"many":[85],"agents":[86,163],"jointly,":[87],"they":[88],"do":[89],"not":[90],"capture":[91],"agent":[92,97,147],"lose":[95],"identities.":[98],"To":[99],"this":[100],"end,":[101],"we":[102,152],"deep":[105],"learning":[106],"architecture":[107],"generates":[109],"Fields":[112],"help":[115],"trace":[120],"loss":[121],"establishes":[123],"consistency":[124],"between":[125],"predictions.":[130],"demonstrate":[132],"effectiveness":[134],"our":[136,198],"approach":[137],"using":[138],"three":[139],"metrics":[140],"on":[141,183],"prediction,":[143],"estimation,":[145],"ID":[148],"recovery.":[149],"In":[150],"addition,":[151],"introduce":[153],"problem":[155],"predicting":[157],"speculative":[158],"which":[160],"are":[161],"currently-occluded":[162],"may":[165],"appear":[166],"future":[169],"through":[170],"dis-occlusion":[171],"or":[172],"entering":[174],"field":[176],"view.":[178],"report":[180],"experimental":[181],"results":[182],"large":[185],"in-house":[186],"driving":[188],"dataset":[189],"public":[192],"INTERACTION":[193],"dataset,":[194],"show":[196],"model":[199],"outperforms":[200],"state-of-the-art":[201],"models.":[202]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":17},{"year":2024,"cited_by_count":27},{"year":2023,"cited_by_count":23},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
