{"id":"https://openalex.org/W2789884076","doi":"https://doi.org/10.1145/3173386.3176972","title":"Anticipating Maneuvers with Dilated Convolutions","display_name":"Anticipating Maneuvers with Dilated Convolutions","publication_year":2018,"publication_date":"2018-03-01","ids":{"openalex":"https://openalex.org/W2789884076","doi":"https://doi.org/10.1145/3173386.3176972","mag":"2789884076"},"language":"en","primary_location":{"id":"doi:10.1145/3173386.3176972","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3173386.3176972","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion 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/A5028317680","display_name":"Banafsheh Rekabdar","orcid":null},"institutions":[{"id":"https://openalex.org/I110378019","display_name":"Southern Illinois University Carbondale","ror":"https://ror.org/049kefs16","country_code":"US","type":"education","lineage":["https://openalex.org/I110378019","https://openalex.org/I2801502357"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Banafsheh Rekabdar","raw_affiliation_strings":["Southern Illinois University, Carbondale, IL, USA"],"affiliations":[{"raw_affiliation_string":"Southern Illinois University, Carbondale, IL, USA","institution_ids":["https://openalex.org/I110378019"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5028317680"],"corresponding_institution_ids":["https://openalex.org/I110378019"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.01995734,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"213","last_page":"214"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9990000128746033,"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":0.9975000023841858,"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/anticipation","display_name":"Anticipation (artificial intelligence)","score":0.8536809682846069},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7494057416915894},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6484704613685608},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.5641193389892578},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5419820547103882},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.5410749316215515},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5163527727127075},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.48793837428092957},{"id":"https://openalex.org/keywords/global-positioning-system","display_name":"Global Positioning System","score":0.4341215491294861},{"id":"https://openalex.org/keywords/time-domain","display_name":"Time domain","score":0.42997997999191284},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.07600405812263489},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.0716409683227539}],"concepts":[{"id":"https://openalex.org/C176777502","wikidata":"https://www.wikidata.org/wiki/Q4774623","display_name":"Anticipation (artificial intelligence)","level":2,"score":0.8536809682846069},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7494057416915894},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6484704613685608},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.5641193389892578},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5419820547103882},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.5410749316215515},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5163527727127075},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.48793837428092957},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.4341215491294861},{"id":"https://openalex.org/C103824480","wikidata":"https://www.wikidata.org/wiki/Q185889","display_name":"Time domain","level":2,"score":0.42997997999191284},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.07600405812263489},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0716409683227539},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3173386.3176972","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3173386.3176972","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.6299999952316284,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W744651418","https://openalex.org/W1789187189","https://openalex.org/W1987616874","https://openalex.org/W2088604406","https://openalex.org/W2162508706","https://openalex.org/W2331033561","https://openalex.org/W2573157838","https://openalex.org/W2963140597","https://openalex.org/W2963692464"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2626256601","https://openalex.org/W147410782","https://openalex.org/W2900413183","https://openalex.org/W4390975304","https://openalex.org/W2150761772","https://openalex.org/W4213201576","https://openalex.org/W3022252430","https://openalex.org/W4287804464","https://openalex.org/W3103989898"],"abstract_inverted_index":{"Anticipation":[0],"is":[1,58],"an":[2],"essential":[3],"ability":[4],"for":[5,9,116],"any":[6],"system":[7],"designed":[8],"human":[10,14],"robot":[11],"interaction.":[12],"As":[13],"activities":[15],"are":[16,33],"complex,":[17],"the":[18,66,105,123],"robot/machine":[19],"should":[20],"be":[21,73],"capable":[22],"of":[23,65,96,125],"processing":[24],"long":[25,46],"time-series":[26],"observations":[27,32],"to":[28,60,100,112],"understand":[29,113],"them.":[30],"These":[31],"normally":[34],"high":[35,41],"dimensional,":[36],"corrupted,":[37],"noisy,":[38],"have":[39,44],"a":[40,52],"frequency,":[42],"and":[43,79,98,108],"very":[45],"temporal":[47,114],"relationships.":[48],"In":[49,89],"this":[50,70,138],"paper":[51],"new":[53],"deep":[54],"learning":[55,135],"model":[56],"architecture":[57],"proposed":[59,91,127],"anticipate":[61],"maneuvers.":[62],"The":[63],"source":[64],"sensory":[67],"data":[68],"in":[69,104,137],"domain":[71],"could":[72,129],"GPS":[74],"location,":[75],"car\u00bbs":[76],"speed,":[77],"inside":[78],"outside":[80],"cameras":[81],"as":[82,84],"well":[83],"other":[85,132],"car":[86],"related":[87],"sensors.":[88],"our":[90,126],"model,":[92],"we":[93],"use":[94],"pairs":[95],"max-pooling":[97],"convolutions":[99,111],"represent":[101],"spatial":[102],"dependencies":[103],"video":[106],"frames":[107],"apply":[109],"dilated":[110],"relationships":[115],"maneuver":[117],"anticipation.":[118],"We":[119],"also":[120],"show":[121],"that":[122],"performance":[124],"approach":[128],"compete":[130],"with":[131],"well-known":[133],"machine":[134],"architectures":[136],"domain.":[139]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
