{"id":"https://openalex.org/W4408258652","doi":"https://doi.org/10.1109/ccwc62904.2025.10903866","title":"Trajectory Prediction in Autonomous Driving: A Comprehensive Review of Deep Learning Models and Future Direction","display_name":"Trajectory Prediction in Autonomous Driving: A Comprehensive Review of Deep Learning Models and Future Direction","publication_year":2025,"publication_date":"2025-01-06","ids":{"openalex":"https://openalex.org/W4408258652","doi":"https://doi.org/10.1109/ccwc62904.2025.10903866"},"language":"en","primary_location":{"id":"doi:10.1109/ccwc62904.2025.10903866","doi":"https://doi.org/10.1109/ccwc62904.2025.10903866","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccwc62904.2025.10903866","pdf_url":null,"source":{"id":"https://openalex.org/S4363608098","display_name":"IEEE Annual Computing and Communication Workshop and Conference (CCWC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"2025 IEEE 15th Annual Computing and Communication Workshop and Conference (CCWC)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5116561381","display_name":"Siddharth Raina","orcid":"https://orcid.org/0009-0001-8745-0199","observed_orcids":["https://orcid.org/0009-0001-8745-0199"]},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Siddharth Raina","raw_affiliation_strings":["Carnegie Mellon University,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University,USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093917868","display_name":"Jeshwanth Challagundla","orcid":"https://orcid.org/0009-0005-5106-3743","observed_orcids":["https://orcid.org/0009-0005-5106-3743"]},"institutions":[{"id":"https://openalex.org/I189196454","display_name":"The University of Texas at Arlington","ror":"https://ror.org/019kgqr73","country_code":"US","type":"education","lineage":["https://openalex.org/I189196454"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jeshwanth Challagundla","raw_affiliation_strings":["University of Texas at Arlington,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Texas at Arlington,USA","institution_ids":["https://openalex.org/I189196454"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012614878","display_name":"M. P. Singh","orcid":"https://orcid.org/0000-0003-3043-1461","observed_orcids":["https://orcid.org/0000-0003-3043-1461"]},"institutions":[{"id":"https://openalex.org/I63098007","display_name":"Liverpool John Moores University","ror":"https://ror.org/04zfme737","country_code":"GB","type":"education","lineage":["https://openalex.org/I63098007"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Mantek Singh","raw_affiliation_strings":["Liverpool John Moores University,UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Liverpool John Moores University,UK","institution_ids":["https://openalex.org/I63098007"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":8.2201,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.97990393,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"00753","last_page":"00759"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9990000128746033,"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"}},"topics":[{"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.00019999999494757503,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10370","display_name":"Traffic and Road Safety","score":9.999999747378752e-05,"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/trajectory-prediction","display_name":"trajectory prediction","score":0.9649999737739563},{"id":"https://openalex.org/keywords/autonomous-driving","display_name":"autonomous driving","score":0.9900000095367432},{"id":"https://openalex.org/keywords/transformer","display_name":"transformer models","score":0.6779999732971191},{"id":"https://openalex.org/keywords/graph-neural-networks","display_name":"graph neural networks","score":0.8379999995231628},{"id":"https://openalex.org/keywords/multimodal-prediction","display_name":"multimodal prediction","score":0.9269999861717224},{"id":"https://openalex.org/keywords/uncertainty-modeling","display_name":"uncertainty modeling","score":0.8379999995231628},{"id":"https://openalex.org/keywords/real-time-deployment","display_name":"real-time deployment","score":0.7279999852180481},{"id":"https://openalex.org/keywords/collision-avoidance","display_name":"collision avoidance","score":0.6729999780654907}],"concepts":[{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.7645678520202637},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6422418355941772},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5439091324806213},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5294577479362488},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3427124619483948},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","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:10.1109/ccwc62904.2025.10903866","doi":"https://doi.org/10.1109/ccwc62904.2025.10903866","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccwc62904.2025.10903866","pdf_url":null,"source":{"id":"https://openalex.org/S4363608098","display_name":"IEEE Annual Computing and Communication Workshop and Conference (CCWC)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"2025 IEEE 15th Annual Computing and Communication Workshop and Conference (CCWC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W2424778531","https://openalex.org/W2519586580","https://openalex.org/W2607296803","https://openalex.org/W2794787653","https://openalex.org/W2805102305","https://openalex.org/W2894978157","https://openalex.org/W2898900571","https://openalex.org/W2905173465","https://openalex.org/W2925148167","https://openalex.org/W2940718442","https://openalex.org/W2943516367","https://openalex.org/W2954414836","https://openalex.org/W2955189650","https://openalex.org/W2980160556","https://openalex.org/W2991653934","https://openalex.org/W2993977494","https://openalex.org/W3032731803","https://openalex.org/W3035172746","https://openalex.org/W3043910080","https://openalex.org/W3062588417","https://openalex.org/W3081405043","https://openalex.org/W3090789587","https://openalex.org/W3196864007","https://openalex.org/W3204875639","https://openalex.org/W3214950490","https://openalex.org/W4214593147","https://openalex.org/W4383172002"],"related_works":["https://openalex.org/W4412818806","https://openalex.org/W4414097138","https://openalex.org/W7169030668","https://openalex.org/W4413427202","https://openalex.org/W4405490122","https://openalex.org/W4400189345","https://openalex.org/W4226239849","https://openalex.org/W4391309499","https://openalex.org/W7161843364","https://openalex.org/W4404987809"],"abstract_inverted_index":{"Autonomous":[0],"driving":[1],"technologies":[2],"have":[3,95,112],"progressed":[4],"significantly,":[5],"promising":[6],"enhanced":[7],"transportation":[8],"safety,":[9],"reduced":[10],"congestion,":[11],"and":[12,41,56,80,91,102,132,151,173],"improved":[13],"mobility.":[14],"A":[15],"critical":[16],"aspect":[17],"of":[18,34,71,142,158,167,176],"these":[19,123],"systems":[20],"is":[21,46],"trajectory":[22,44,62,144],"prediction,":[23],"which":[24],"allows":[25],"autonomous":[26,159],"vehicles":[27],"(AVs)":[28],"to":[29,50,67],"forecast":[30],"the":[31,68,156,165,171,174],"future":[32,152],"movements":[33],"surrounding":[35],"agents,":[36],"such":[37,126],"as":[38,114,127],"vehicles,":[39],"pedestrians,":[40],"cyclists.":[42],"Accurate":[43],"prediction":[45,63,93],"essential":[47],"for":[48,118,179],"AVs":[49],"avoid":[51],"collisions,":[52],"make":[53],"informed":[54],"decisions,":[55],"navigate":[57],"complex":[58,100],"environments.":[59,106],"However,":[60],"real-world":[61,180],"remains":[64],"challenging":[65],"due":[66],"dynamic":[69,105],"nature":[70],"traffic":[72,78],"environments,":[73],"influenced":[74],"by":[75],"road":[76],"geometry,":[77],"regulations,":[79],"agent":[81],"interactions.":[82],"Recent":[83],"advancements":[84],"in":[85,98,104,155,169],"deep":[86],"learning,":[87],"particularly":[88],"transformer-based":[89],"architectures":[90],"multimodal":[92],"techniques,":[94],"shown":[96],"promise":[97],"modeling":[99],"interactions":[101],"uncertainty":[103],"Furthermore,":[107],"Graph":[108],"Neural":[109],"Networks":[110],"(GNNs)":[111],"emerged":[113],"a":[115,139],"key":[116],"tool":[117],"capturing":[119],"multi-agent":[120],"dependencies.":[121],"Despite":[122],"developments,":[124],"challenges":[125],"scalability,":[128],"handling":[129],"edge":[130],"cases,":[131],"real-time":[133],"performance":[134],"persist.":[135],"This":[136],"paper":[137],"provides":[138],"comprehensive":[140],"review":[141,162],"state-of-the-art":[143],"forecasting":[145],"models,":[146],"discussing":[147],"their":[148],"strengths,":[149],"limitations,":[150],"research":[153],"directions":[154],"context":[157],"driving.":[160],"The":[161],"also":[163],"highlights":[164],"role":[166],"datasets":[168],"advancing":[170],"field":[172],"importance":[175],"optimizing":[177],"models":[178],"deployment.":[181]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":4}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2025-03-09T00:00:00"}