{"id":"https://openalex.org/W3110317294","doi":"https://doi.org/10.1109/iv51971.2022.9827055","title":"Multi-Modal Hybrid Architecture for Pedestrian Action Prediction","display_name":"Multi-Modal Hybrid Architecture for Pedestrian Action Prediction","publication_year":2022,"publication_date":"2022-06-05","ids":{"openalex":"https://openalex.org/W3110317294","doi":"https://doi.org/10.1109/iv51971.2022.9827055","mag":"3110317294"},"language":"en","primary_location":{"id":"doi:10.1109/iv51971.2022.9827055","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv51971.2022.9827055","pdf_url":null,"source":{"id":"https://openalex.org/S4363605370","display_name":"2022 IEEE Intelligent Vehicles Symposium (IV)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE Intelligent Vehicles Symposium (IV)","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/A5012752787","display_name":"Amir Rasouli","orcid":"https://orcid.org/0000-0002-0106-2225"},"institutions":[{"id":"https://openalex.org/I4210115038","display_name":"Huawei Technologies (Canada)","ror":"https://ror.org/026venb53","country_code":"CA","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210115038"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Amir Rasouli","raw_affiliation_strings":["Noah&#x2019;s Ark Laboratory,Huawei,Markham,Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Noah&#x2019;s Ark Laboratory,Huawei,Markham,Canada","institution_ids":["https://openalex.org/I4210115038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021866203","display_name":"Tiffany Yau","orcid":null},"institutions":[{"id":"https://openalex.org/I185261750","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087","country_code":"CA","type":"education","lineage":["https://openalex.org/I185261750"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Tiffany Yau","raw_affiliation_strings":["University of Toronto,Toronto,ON,Canada","University of Toronto, Toronto, ON, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Toronto,Toronto,ON,Canada","institution_ids":["https://openalex.org/I185261750"]},{"raw_affiliation_string":"University of Toronto, Toronto, ON, Canada","institution_ids":["https://openalex.org/I185261750"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103552170","display_name":"Mohsen Rohani","orcid":null},"institutions":[{"id":"https://openalex.org/I4210115038","display_name":"Huawei Technologies (Canada)","ror":"https://ror.org/026venb53","country_code":"CA","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210115038"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Mohsen Rohani","raw_affiliation_strings":["Noah&#x2019;s Ark Laboratory,Huawei,Markham,Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Noah&#x2019;s Ark Laboratory,Huawei,Markham,Canada","institution_ids":["https://openalex.org/I4210115038"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081222445","display_name":"Jun Luo","orcid":"https://orcid.org/0000-0002-7036-5158"},"institutions":[{"id":"https://openalex.org/I4210115038","display_name":"Huawei Technologies (Canada)","ror":"https://ror.org/026venb53","country_code":"CA","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210115038"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Jun Luo","raw_affiliation_strings":["Noah&#x2019;s Ark Laboratory,Huawei,Markham,Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Noah&#x2019;s Ark Laboratory,Huawei,Markham,Canada","institution_ids":["https://openalex.org/I4210115038"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":9.1534,"has_fulltext":false,"cited_by_count":49,"citation_normalized_percentile":{"value":0.98965071,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"91","last_page":"97"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9994000196456909,"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":0.9994000196456909,"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":0.9980999827384949,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9958999752998352,"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/pedestrian","display_name":"Pedestrian","score":0.9048163890838623},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7945019006729126},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.6934394836425781},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5339704155921936},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.5008854866027832},{"id":"https://openalex.org/keywords/feed-forward","display_name":"Feed forward","score":0.49891114234924316},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.49765899777412415},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4523010849952698},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.44364798069000244},{"id":"https://openalex.org/keywords/pedestrian-crossing","display_name":"Pedestrian crossing","score":0.42952844500541687},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13200873136520386},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.11363831162452698},{"id":"https://openalex.org/keywords/control-engineering","display_name":"Control engineering","score":0.08698099851608276}],"concepts":[{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.9048163890838623},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7945019006729126},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.6934394836425781},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5339704155921936},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.5008854866027832},{"id":"https://openalex.org/C38858127","wikidata":"https://www.wikidata.org/wiki/Q5441228","display_name":"Feed forward","level":2,"score":0.49891114234924316},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.49765899777412415},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4523010849952698},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44364798069000244},{"id":"https://openalex.org/C2777819797","wikidata":"https://www.wikidata.org/wiki/Q8010","display_name":"Pedestrian crossing","level":3,"score":0.42952844500541687},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13200873136520386},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.11363831162452698},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.08698099851608276},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C153349607","wikidata":"https://www.wikidata.org/wiki/Q36649","display_name":"Visual arts","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iv51971.2022.9827055","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv51971.2022.9827055","pdf_url":null,"source":{"id":"https://openalex.org/S4363605370","display_name":"2022 IEEE Intelligent Vehicles Symposium (IV)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.8399999737739563}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":67,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W1902237438","https://openalex.org/W1923404803","https://openalex.org/W1970206276","https://openalex.org/W2004641798","https://openalex.org/W2101415982","https://openalex.org/W2340897893","https://openalex.org/W2519586580","https://openalex.org/W2559085405","https://openalex.org/W2592496287","https://openalex.org/W2630837129","https://openalex.org/W2771583656","https://openalex.org/W2780253814","https://openalex.org/W2798952702","https://openalex.org/W2807456624","https://openalex.org/W2883770893","https://openalex.org/W2894978157","https://openalex.org/W2898900571","https://openalex.org/W2905385096","https://openalex.org/W2912945584","https://openalex.org/W2913368959","https://openalex.org/W2949767467","https://openalex.org/W2962701817","https://openalex.org/W2963392613","https://openalex.org/W2963524571","https://openalex.org/W2963610939","https://openalex.org/W2963697717","https://openalex.org/W2963737762","https://openalex.org/W2964185119","https://openalex.org/W2964199361","https://openalex.org/W2967177252","https://openalex.org/W2968524820","https://openalex.org/W2968684599","https://openalex.org/W2991484432","https://openalex.org/W3008700642","https://openalex.org/W3010309798","https://openalex.org/W3022272795","https://openalex.org/W3034630387","https://openalex.org/W3034734814","https://openalex.org/W3034782805","https://openalex.org/W3035054225","https://openalex.org/W3035172263","https://openalex.org/W3035172746","https://openalex.org/W3035574168","https://openalex.org/W3035671534","https://openalex.org/W3035692480","https://openalex.org/W3039524844","https://openalex.org/W3106824422","https://openalex.org/W3107046451","https://openalex.org/W3109667662","https://openalex.org/W3111132231","https://openalex.org/W3112125472","https://openalex.org/W3119170582","https://openalex.org/W3119361198","https://openalex.org/W4287778673","https://openalex.org/W4297795828","https://openalex.org/W6639102338","https://openalex.org/W6651503529","https://openalex.org/W6674955169","https://openalex.org/W6739696289","https://openalex.org/W6752527605","https://openalex.org/W6755864109","https://openalex.org/W6759079449","https://openalex.org/W6776598532","https://openalex.org/W6779988093","https://openalex.org/W6787076206","https://openalex.org/W6787109298"],"related_works":["https://openalex.org/W2972620127","https://openalex.org/W187110833","https://openalex.org/W2981141433","https://openalex.org/W122740207","https://openalex.org/W3146891537","https://openalex.org/W4388221821","https://openalex.org/W650967530","https://openalex.org/W4390813505","https://openalex.org/W2164690004","https://openalex.org/W2047776971"],"abstract_inverted_index":{"Pedestrian":[0],"behavior":[1,108],"prediction":[2,57,127],"is":[3],"one":[4],"of":[5,22,27,33,40,63,74,86,95,100],"the":[6,44,67,96,101,104],"major":[7],"challenges":[8],"for":[9,91],"intelligent":[10],"driving":[11,113],"systems":[12],"in":[13,124],"urban":[14],"environments.":[15],"Pedestrians":[16],"often":[17],"exhibit":[18],"a":[19,54,81,111],"wide":[20],"range":[21],"behaviors":[23],"and":[24,88,98,110],"adequate":[25],"interpretations":[26],"those":[28],"depend":[29],"on":[30],"various":[31],"sources":[32,62],"information":[34,64],"such":[35],"as":[36],"pedestrian":[37,107,125],"appearance,":[38],"states":[39],"other":[41],"road":[42],"users,":[43],"environment":[45,68,97],"layout,":[46],"etc.":[47],"To":[48],"address":[49],"this":[50],"problem,":[51],"we":[52,115],"propose":[53],"novel":[55],"multi-modal":[56],"algorithm":[58],"that":[59,117],"incorporates":[60],"different":[61],"captured":[65],"from":[66,80],"to":[69],"predict":[70],"future":[71],"crossing":[72,126],"actions":[73],"pedestrians.":[75],"The":[76],"proposed":[77,119],"model":[78,120],"benefits":[79],"hybrid":[82],"learning":[83],"architecture":[84],"consisting":[85],"feedforward":[87],"recurrent":[89],"networks":[90],"analyzing":[92],"visual":[93],"features":[94],"dynamics":[99],"scene.":[102],"Using":[103],"existing":[105],"2D":[106],"benchmarks":[109],"3D":[112],"dataset,":[114],"show":[116],"our":[118],"achieves":[121],"state-of-the-art":[122],"performance":[123]},"counts_by_year":[{"year":2025,"cited_by_count":16},{"year":2024,"cited_by_count":13},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":6}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
