{"id":"https://openalex.org/W4404294891","doi":"https://doi.org/10.1109/m2vip62491.2024.10746073","title":"Pedestrian Trajectory Prediction Based on Multimodal Fusion of Motion Sensing Encoder Decoder Network","display_name":"Pedestrian Trajectory Prediction Based on Multimodal Fusion of Motion Sensing Encoder Decoder Network","publication_year":2024,"publication_date":"2024-10-03","ids":{"openalex":"https://openalex.org/W4404294891","doi":"https://doi.org/10.1109/m2vip62491.2024.10746073"},"language":"en","primary_location":{"id":"doi:10.1109/m2vip62491.2024.10746073","is_oa":false,"landing_page_url":"https://doi.org/10.1109/m2vip62491.2024.10746073","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 30th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5101596189","display_name":"Zhiqiang Tang","orcid":"https://orcid.org/0000-0003-0710-3056"},"institutions":[{"id":"https://openalex.org/I91125648","display_name":"Wuhan Institute of Technology","ror":"https://ror.org/04jcykh16","country_code":"CN","type":"education","lineage":["https://openalex.org/I91125648"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiqiang Tang","raw_affiliation_strings":["Wuhan Institute of Technology,Wuhan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wuhan Institute of Technology,Wuhan,China","institution_ids":["https://openalex.org/I91125648"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037176711","display_name":"Wenxia Xu","orcid":"https://orcid.org/0000-0002-1101-418X"},"institutions":[{"id":"https://openalex.org/I91125648","display_name":"Wuhan Institute of Technology","ror":"https://ror.org/04jcykh16","country_code":"CN","type":"education","lineage":["https://openalex.org/I91125648"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenxia Xu","raw_affiliation_strings":["Wuhan Institute of Technology,Wuhan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wuhan Institute of Technology,Wuhan,China","institution_ids":["https://openalex.org/I91125648"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101050018","display_name":"Baocheng Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I91125648","display_name":"Wuhan Institute of Technology","ror":"https://ror.org/04jcykh16","country_code":"CN","type":"education","lineage":["https://openalex.org/I91125648"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Baocheng Yu","raw_affiliation_strings":["Wuhan Institute of Technology,Wuhan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wuhan Institute of Technology,Wuhan,China","institution_ids":["https://openalex.org/I91125648"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052681440","display_name":"Jian Huang","orcid":"https://orcid.org/0000-0001-8651-814X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jian Huang","raw_affiliation_strings":["Huazhong University of Science and Technology,United Kingdom Great Britain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Huazhong University of Science and Technology,United Kingdom Great Britain","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029939473","display_name":"Xinxing Chen","orcid":"https://orcid.org/0000-0002-6265-1226"},"institutions":[{"id":"https://openalex.org/I3045169105","display_name":"Southern University of Science and Technology","ror":"https://ror.org/049tv2d57","country_code":"CN","type":"education","lineage":["https://openalex.org/I3045169105"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinxing Chen","raw_affiliation_strings":["Southern University of Science and Technology,Shenzhen,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Southern University of Science and Technology,Shenzhen,China","institution_ids":["https://openalex.org/I3045169105"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5011739311","display_name":"Hai He","orcid":"https://orcid.org/0000-0001-7246-1197"},"institutions":[{"id":"https://openalex.org/I91125648","display_name":"Wuhan Institute of Technology","ror":"https://ror.org/04jcykh16","country_code":"CN","type":"education","lineage":["https://openalex.org/I91125648"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hanyan He","raw_affiliation_strings":["Wuhan Institute of Technology,Wuhan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wuhan Institute of Technology,Wuhan,China","institution_ids":["https://openalex.org/I91125648"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.8787999749183655,"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"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.8787999749183655,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.8726999759674072,"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/T13282","display_name":"Automated Road and Building Extraction","score":0.8222000002861023,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7606072425842285},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.6931308507919312},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.6704660654067993},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.634778618812561},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5335659980773926},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5300576090812683},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.4549699127674103},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.43397560715675354},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.43113014101982117},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.328707218170166},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11601722240447998}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7606072425842285},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.6931308507919312},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.6704660654067993},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.634778618812561},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5335659980773926},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5300576090812683},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.4549699127674103},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.43397560715675354},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.43113014101982117},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.328707218170166},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11601722240447998},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/m2vip62491.2024.10746073","is_oa":false,"landing_page_url":"https://doi.org/10.1109/m2vip62491.2024.10746073","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 30th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)","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":0.6200000047683716}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W2064675550","https://openalex.org/W2079735306","https://openalex.org/W2188365844","https://openalex.org/W2245440101","https://openalex.org/W2424778531","https://openalex.org/W2532516272","https://openalex.org/W2560674852","https://openalex.org/W2604216058","https://openalex.org/W2619383789","https://openalex.org/W2766836212","https://openalex.org/W2767290858","https://openalex.org/W2771583656","https://openalex.org/W2803184913","https://openalex.org/W2883445328","https://openalex.org/W2883770893","https://openalex.org/W2962687116","https://openalex.org/W2962730651","https://openalex.org/W2962855257","https://openalex.org/W2963001155","https://openalex.org/W2963076818","https://openalex.org/W2963165299","https://openalex.org/W2964136016","https://openalex.org/W2966926453","https://openalex.org/W2991484432","https://openalex.org/W3012055358","https://openalex.org/W3033920763","https://openalex.org/W3065468121","https://openalex.org/W3105115779","https://openalex.org/W3106257603","https://openalex.org/W3108908812","https://openalex.org/W3127710918","https://openalex.org/W3176739692","https://openalex.org/W3184258555","https://openalex.org/W4312517993","https://openalex.org/W4393078713","https://openalex.org/W6686207219","https://openalex.org/W6687045409"],"related_works":["https://openalex.org/W2392100589","https://openalex.org/W2512789322","https://openalex.org/W3122828758","https://openalex.org/W2101960027","https://openalex.org/W4205958986","https://openalex.org/W2132659060","https://openalex.org/W2031992971","https://openalex.org/W3214791684","https://openalex.org/W2353265673","https://openalex.org/W2152662039"],"abstract_inverted_index":{"Aiming":[0],"at":[1],"the":[2,14,43,65,74,91,97,104,119,122,142,151,163,166],"problem":[3],"that":[4,150],"traditional":[5],"pedestrian":[6,61,68,94],"trajectory":[7,71,132],"prediction":[8,83,130,133],"tasks":[9,125],"are":[10,19,84,87,134],"mostly":[11],"concentrated":[12],"in":[13,42],"bird":[15],"'s-eye":[16],"view":[17],"and":[18,60,63,81,96,114,131,161,169],"not":[20],"suitable":[21],"for":[22],"practical":[23],"applications":[24],"such":[25,52],"as":[26,53],"robots,":[27],"this":[28],"paper":[29],"proposes":[30],"a":[31,137],"motion":[32,59,69,170],"awareness":[33],"encoder":[34],"decoder":[35,102],"network":[36],"framework":[37,47,140],"based":[38],"on":[39,70,157],"multi-modal":[40,167],"fusion":[41,168],"first":[44],"perspective.":[45],"The":[46,101,146],"integrates":[48],"multi-source":[49],"heterogeneous":[50],"information":[51,117],"visual":[54],"image,":[55],"geometric":[56],"position,":[57],"robot":[58],"posture,":[62],"shows":[64],"influence":[66],"of":[67,78,93,118,126,165],"prediction.":[72],"In":[73],"encoder,":[75],"two":[76],"branches":[77],"target":[79,99,127],"estimation":[80],"action":[82,95,129],"designed,":[85],"which":[86,107],"used":[88],"to":[89,110],"perceive":[90],"intention":[92],"future":[98],"position.":[100],"adopts":[103],"Bi-LSTM":[105],"architecture,":[106],"is":[108],"able":[109],"capture":[111],"both":[112],"forward":[113],"back-up":[115],"contextual":[116],"sequence.":[120],"Finally,":[121],"three":[123],"related":[124],"estimation,":[128],"integrated":[135],"into":[136],"joint":[138],"training":[139],"through":[141],"multi-task":[143],"loss":[144],"function.":[145],"experimental":[147],"results":[148],"show":[149],"proposed":[152],"method":[153],"achieves":[154],"excellent":[155],"performance":[156],"several":[158],"standard":[159],"indexes,":[160],"verifies":[162],"effectiveness":[164],"perception":[171],"strategy.":[172]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}