{"id":"https://openalex.org/W3206121233","doi":"https://doi.org/10.1109/icra48506.2021.9561536","title":"Ellipse Loss for Scene-Compliant Motion Prediction","display_name":"Ellipse Loss for Scene-Compliant Motion Prediction","publication_year":2021,"publication_date":"2021-05-30","ids":{"openalex":"https://openalex.org/W3206121233","doi":"https://doi.org/10.1109/icra48506.2021.9561536","mag":"3206121233"},"language":"en","primary_location":{"id":"doi:10.1109/icra48506.2021.9561536","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra48506.2021.9561536","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101059479","display_name":"Henggang Cui","orcid":null},"institutions":[{"id":"https://openalex.org/I4210123843","display_name":"Advanced Technologies Group (United States)","ror":"https://ror.org/0359sgh16","country_code":"US","type":"company","lineage":["https://openalex.org/I4210123843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Henggang Cui","raw_affiliation_strings":["Uber Advanced Technologies Group (ATG), Pittsburgh, PA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Uber Advanced Technologies Group (ATG), Pittsburgh, PA","institution_ids":["https://openalex.org/I4210123843"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075638671","display_name":"Hoda Shajari","orcid":null},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hoda Shajari","raw_affiliation_strings":["Uber ATG, University of Florida, Work Done During Internship"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Uber ATG, University of Florida, Work Done During Internship","institution_ids":["https://openalex.org/I33213144"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020070465","display_name":"Sai Yalamanchi","orcid":null},"institutions":[{"id":"https://openalex.org/I4210123843","display_name":"Advanced Technologies Group (United States)","ror":"https://ror.org/0359sgh16","country_code":"US","type":"company","lineage":["https://openalex.org/I4210123843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sai Yalamanchi","raw_affiliation_strings":["Uber Advanced Technologies Group (ATG), Pittsburgh, PA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Uber Advanced Technologies Group (ATG), Pittsburgh, PA","institution_ids":["https://openalex.org/I4210123843"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082498217","display_name":"Nemanja Djuric","orcid":"https://orcid.org/0000-0002-6502-7891"},"institutions":[{"id":"https://openalex.org/I4210123843","display_name":"Advanced Technologies Group (United States)","ror":"https://ror.org/0359sgh16","country_code":"US","type":"company","lineage":["https://openalex.org/I4210123843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nemanja Djuric","raw_affiliation_strings":["Uber Advanced Technologies Group (ATG), Pittsburgh, PA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Uber Advanced Technologies Group (ATG), Pittsburgh, PA","institution_ids":["https://openalex.org/I4210123843"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3719,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.6011762,"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":1.0,"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":1.0,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10370","display_name":"Traffic and Road Safety","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/ellipse","display_name":"Ellipse","score":0.9241350889205933},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7683850526809692},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.768152117729187},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.639281153678894},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5478435158729553},{"id":"https://openalex.org/keywords/orientation","display_name":"Orientation (vector space)","score":0.5316981077194214},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.5308051109313965},{"id":"https://openalex.org/keywords/differentiable-function","display_name":"Differentiable function","score":0.45121482014656067},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.4479868710041046},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4373076260089874},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13073357939720154},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11769607663154602},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.0745718777179718}],"concepts":[{"id":"https://openalex.org/C74261601","wikidata":"https://www.wikidata.org/wiki/Q40112","display_name":"Ellipse","level":2,"score":0.9241350889205933},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7683850526809692},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.768152117729187},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.639281153678894},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5478435158729553},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.5316981077194214},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.5308051109313965},{"id":"https://openalex.org/C202615002","wikidata":"https://www.wikidata.org/wiki/Q783507","display_name":"Differentiable function","level":2,"score":0.45121482014656067},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.4479868710041046},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4373076260089874},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13073357939720154},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11769607663154602},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0745718777179718},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icra48506.2021.9561536","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra48506.2021.9561536","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6000000238418579,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W2032924574","https://openalex.org/W2607296803","https://openalex.org/W2798930779","https://openalex.org/W2898900571","https://openalex.org/W2905173465","https://openalex.org/W2940129212","https://openalex.org/W2962687116","https://openalex.org/W2963001155","https://openalex.org/W2963120444","https://openalex.org/W2967177252","https://openalex.org/W2968008415","https://openalex.org/W2970219816","https://openalex.org/W2970971581","https://openalex.org/W2973442783","https://openalex.org/W2980160556","https://openalex.org/W2981084009","https://openalex.org/W2985871763","https://openalex.org/W3000520642","https://openalex.org/W3007298738","https://openalex.org/W3010072020","https://openalex.org/W3011865257","https://openalex.org/W3016419078","https://openalex.org/W3028769608","https://openalex.org/W3029177463","https://openalex.org/W3033207664","https://openalex.org/W3034295100","https://openalex.org/W3080700777","https://openalex.org/W3090789587","https://openalex.org/W3114588740","https://openalex.org/W3114753236","https://openalex.org/W3117796851","https://openalex.org/W3121034396","https://openalex.org/W3209693651","https://openalex.org/W4288287716","https://openalex.org/W4295312788","https://openalex.org/W6755864109","https://openalex.org/W6756871163","https://openalex.org/W6764068339","https://openalex.org/W6765361892","https://openalex.org/W6766978945","https://openalex.org/W6767069752","https://openalex.org/W6768159682","https://openalex.org/W6769043036","https://openalex.org/W6769347387","https://openalex.org/W6769377150","https://openalex.org/W6773443883","https://openalex.org/W6774593552","https://openalex.org/W6774797786","https://openalex.org/W6779332932"],"related_works":["https://openalex.org/W2371136327","https://openalex.org/W4293234107","https://openalex.org/W2370549269","https://openalex.org/W2386824811","https://openalex.org/W2354618530","https://openalex.org/W2081226903","https://openalex.org/W2363452175","https://openalex.org/W2375225935","https://openalex.org/W2542179972","https://openalex.org/W2131998656"],"abstract_inverted_index":{"Motion":[0],"prediction":[1,29],"is":[2],"a":[3,50,76,90,117],"critical":[4],"part":[5],"of":[6,14],"self-driving":[7],"technology,":[8],"responsible":[9],"for":[10,140],"inferring":[11],"future":[12],"behavior":[13],"traffic":[15],"actors":[16],"in":[17,75],"autonomous":[18,131],"vehicle\u2019s":[19],"surroundings.":[20],"In":[21,41],"order":[22],"to":[23,32,58,109,116,124],"ensure":[24],"safe":[25],"and":[26,48,64,102,143],"efficient":[27],"operations,":[28],"models":[30,57],"need":[31],"output":[33,82],"accurate":[34,142],"trajectories":[35,83],"that":[36,54,136],"obey":[37],"the":[38,56,81,85,110,137],"map":[39,87],"constraints.":[40],"this":[42,46],"paper,":[43],"we":[44],"address":[45],"task":[47],"propose":[49],"novel":[51],"ellipse":[52,114],"loss":[53,70,115],"allows":[55,139],"better":[59],"reason":[60],"about":[61],"scene":[62],"compliance":[63],"predict":[65],"more":[66,105,141,144],"realistic":[67,145],"trajectories.":[68],"Ellipse":[69],"penalizes":[71],"off-road":[72],"predictions":[73],"directly":[74],"supervised":[77],"manner,":[78],"by":[79],"projecting":[80],"into":[84,98],"top-down":[86],"frame":[88],"using":[89],"differentiable":[91],"trajectory":[92,146],"rasterizer":[93],"module.":[94],"Moreover,":[95],"it":[96],"takes":[97],"account":[99],"actor":[100],"dimensions":[101],"orientation,":[103],"providing":[104],"direct":[106],"training":[107],"signals":[108],"model.":[111],"We":[112],"applied":[113],"recently":[118],"proposed":[119],"state-of-the-art":[120],"joint":[121],"detection-prediction":[122],"model":[123],"showcase":[125],"its":[126],"benefits.":[127],"Evaluation":[128],"on":[129],"large-scale":[130],"driving":[132],"data":[133],"strongly":[134],"indicates":[135],"method":[138],"predictions.":[147]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
