{"id":"https://openalex.org/W2791581064","doi":"https://doi.org/10.1145/3171221.3171255","title":"Social Momentum","display_name":"Social Momentum","publication_year":2018,"publication_date":"2018-02-26","ids":{"openalex":"https://openalex.org/W2791581064","doi":"https://doi.org/10.1145/3171221.3171255","mag":"2791581064"},"language":"en","primary_location":{"id":"doi:10.1145/3171221.3171255","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3171221.3171255","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction","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/A5067086333","display_name":"Christoforos Mavrogiannis","orcid":"https://orcid.org/0000-0003-4476-1920"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Christoforos I. Mavrogiannis","raw_affiliation_strings":["Cornell University, Ithaca, NY, USA"],"affiliations":[{"raw_affiliation_string":"Cornell University, Ithaca, NY, USA","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070481627","display_name":"Wil Thomason","orcid":"https://orcid.org/0000-0001-6200-9762"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wil B. Thomason","raw_affiliation_strings":["Cornell University, Ithaca, NY, USA"],"affiliations":[{"raw_affiliation_string":"Cornell University, Ithaca, NY, USA","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5027264368","display_name":"Ross A. Knepper","orcid":"https://orcid.org/0000-0003-0462-5502"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ross A. Knepper","raw_affiliation_strings":["Cornell University, Ithaca, NY, USA"],"affiliations":[{"raw_affiliation_string":"Cornell University, Ithaca, NY, USA","institution_ids":["https://openalex.org/I205783295"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5067086333"],"corresponding_institution_ids":["https://openalex.org/I205783295"],"apc_list":null,"apc_paid":null,"fwci":6.23,"has_fulltext":false,"cited_by_count":47,"citation_normalized_percentile":{"value":0.96565446,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"361","last_page":"369"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11500","display_name":"Evacuation and Crowd Dynamics","score":0.9979000091552734,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T11500","display_name":"Evacuation and Crowd Dynamics","score":0.9979000091552734,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9908999800682068,"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/T10586","display_name":"Robotic Path Planning Algorithms","score":0.9882000088691711,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6822291612625122},{"id":"https://openalex.org/keywords/collision-avoidance","display_name":"Collision avoidance","score":0.6408565044403076},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.6219486594200134},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6203271746635437},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6081816554069519},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.5970277786254883},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.5118776559829712},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.49428772926330566},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4913162291049957},{"id":"https://openalex.org/keywords/motion-planning","display_name":"Motion planning","score":0.421597421169281},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.4106219708919525},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33853527903556824},{"id":"https://openalex.org/keywords/collision","display_name":"Collision","score":0.2339874505996704},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.1387273371219635},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12520620226860046}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6822291612625122},{"id":"https://openalex.org/C2780864053","wikidata":"https://www.wikidata.org/wiki/Q5147495","display_name":"Collision avoidance","level":3,"score":0.6408565044403076},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.6219486594200134},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6203271746635437},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6081816554069519},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.5970277786254883},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.5118776559829712},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.49428772926330566},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4913162291049957},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.421597421169281},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.4106219708919525},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33853527903556824},{"id":"https://openalex.org/C121704057","wikidata":"https://www.wikidata.org/wiki/Q352070","display_name":"Collision","level":2,"score":0.2339874505996704},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.1387273371219635},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12520620226860046},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3171221.3171255","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3171221.3171255","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5600000023841858,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G278046944","display_name":null,"funder_award_id":"IIS-1526035","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W192919555","https://openalex.org/W1525312156","https://openalex.org/W1820081798","https://openalex.org/W1900532003","https://openalex.org/W1970650938","https://openalex.org/W1998152817","https://openalex.org/W1998716564","https://openalex.org/W1999657641","https://openalex.org/W2011343364","https://openalex.org/W2014767144","https://openalex.org/W2015345667","https://openalex.org/W2017325967","https://openalex.org/W2026000422","https://openalex.org/W2046213647","https://openalex.org/W2072013063","https://openalex.org/W2088052029","https://openalex.org/W2088604406","https://openalex.org/W2101821104","https://openalex.org/W2107530627","https://openalex.org/W2117266227","https://openalex.org/W2127736385","https://openalex.org/W2133552987","https://openalex.org/W2140542425","https://openalex.org/W2165619060","https://openalex.org/W2167052694","https://openalex.org/W2210721416","https://openalex.org/W2227909145","https://openalex.org/W2293700449","https://openalex.org/W2415627933","https://openalex.org/W2424778531","https://openalex.org/W2488883109","https://openalex.org/W2588978360","https://openalex.org/W2592031441","https://openalex.org/W2594775242","https://openalex.org/W2604213426","https://openalex.org/W2748490912","https://openalex.org/W2767512510","https://openalex.org/W2773965764","https://openalex.org/W2951731327","https://openalex.org/W3022573402","https://openalex.org/W4246329541"],"related_works":["https://openalex.org/W2392100589","https://openalex.org/W2512789322","https://openalex.org/W2378211422","https://openalex.org/W3122828758","https://openalex.org/W4321353415","https://openalex.org/W2101960027","https://openalex.org/W2745001401","https://openalex.org/W4205958986","https://openalex.org/W2197846993","https://openalex.org/W4381746183"],"abstract_inverted_index":{"Intent-expressive":[0],"robot":[1,23,135],"motion":[2,96],"has":[3],"been":[4],"shown":[5],"to":[6,48,215],"result":[7,191],"in":[8,30,52,158,183],"increased":[9],"efficiency":[10],"and":[11,55,128,132,151],"reduced":[12],"planning":[13,90],"efforts":[14],"for":[15,20,154,192,218],"copresent":[16],"humans.":[17],"Existing":[18],"frameworks":[19],"generating":[21,95],"intent-expressive":[22],"behaviors":[24],"have":[25],"typically":[26],"focused":[27],"on":[28,122],"applications":[29,195],"static":[31],"or":[32],"structured":[33],"environments.":[34],"Under":[35],"such":[36,58],"settings,":[37],"emphasis":[38],"is":[39,68,196],"placed":[40],"towards":[41],"communicating":[42],"the":[43,64,76,113,139,159],"robot\u00bbs":[44,65],"intended":[45,102,116],"final":[46,66],"configuration":[47,67],"other":[49],"agents.":[50,82],"However,":[51],"dynamic,":[53],"unstructured":[54],"multi-agent":[56,184,208],"domains,":[57],"as":[59,72,156],"pedestrian":[60],"environments,":[61],"knowledge":[62],"of":[63,79,119,141,163,189,210],"not":[69],"sufficiently":[70],"informative":[71],"it":[73],"completely":[74],"ignores":[75],"complex":[77],"dynamics":[78],"interaction":[80],"among":[81],"To":[83],"address":[84],"this":[85,190],"problem,":[86],"we":[87],"design":[88],"a":[89,199],"framework":[91,111,171],"that":[92,97,137,169,202,207],"aims":[93],"at":[94],"clearly":[98],"communicates":[99],"an":[100,130],"agent\u00bbs":[101],"collision":[103,185],"avoidance":[104,117,145],"strategy":[105],"rather":[106],"than":[107],"its":[108],"destination.":[109],"Our":[110],"estimates":[112],"most":[114],"likely":[115],"protocols":[118],"others":[120,142],"based":[121],"their":[123],"past":[124],"behaviors,":[125],"superimposes":[126],"them,":[127],"generates":[129],"expressive":[131],"socially":[133],"compliant":[134],"action":[136,148],"reinforces":[138],"expectations":[140],"regarding":[143],"these":[144],"protocols.":[146],"This":[147],"facilitates":[149],"inference":[150,217],"decision":[152],"making":[153],"everyone,":[155],"illustrated":[157],"simplified":[160],"topological":[161,176,212],"pattern":[162],"agents\u00bb":[164],"trajectories.":[165],"Extensive":[166],"simulations":[167],"demonstrate":[168],"our":[170],"consistently":[172],"achieves":[173],"significantly":[174],"lower":[175,211],"complexity,":[177],"compared":[178],"against":[179],"common":[180],"benchmark":[181],"approaches":[182],"avoidance.":[186],"The":[187],"significance":[188],"real":[193],"world":[194],"demonstrated":[197],"by":[198],"user":[200],"study":[201],"reveals":[203],"statistical":[204],"evidence":[205],"suggesting":[206],"trajectories":[209],"complexity":[213],"tend":[214],"facilitate":[216],"observers.":[219]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":15},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2018-03-29T00:00:00"}