{"id":"https://openalex.org/W4399257668","doi":"https://doi.org/10.1145/3653781.3653797","title":"A Vehicle Behavior Prediction Method Based on CNN-Transformer Model","display_name":"A Vehicle Behavior Prediction Method Based on CNN-Transformer Model","publication_year":2024,"publication_date":"2024-01-19","ids":{"openalex":"https://openalex.org/W4399257668","doi":"https://doi.org/10.1145/3653781.3653797"},"language":"en","primary_location":{"id":"doi:10.1145/3653781.3653797","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3653781.3653797","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Computer Vision and Deep Learning","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/A5066737325","display_name":"Xiaoting Shu","orcid":null},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaoting Shu","raw_affiliation_strings":["School of Automation and Intelligence Beijing Jiaotong University, China"],"raw_orcid":"https://orcid.org/0009-0006-7980-9196","affiliations":[{"raw_affiliation_string":"School of Automation and Intelligence Beijing Jiaotong University, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103127011","display_name":"Runmei Li","orcid":"https://orcid.org/0000-0002-9034-1399"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Runmei Li","raw_affiliation_strings":["School of Automation and Intelligence Beijing Jiaotong University, China"],"raw_orcid":"https://orcid.org/0000-0002-9034-1399","affiliations":[{"raw_affiliation_string":"School of Automation and Intelligence Beijing Jiaotong University, China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5066737325"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":0.1937,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.45227882,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"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/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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9944999814033508,"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/T12095","display_name":"Vehicle emissions and performance","score":0.9940000176429749,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.8848915100097656},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.7483706474304199},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7141028642654419},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6003091931343079},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.582055389881134},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4502526819705963},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4294130206108093},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4148104190826416},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.21701601147651672},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.10855203866958618}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.8848915100097656},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.7483706474304199},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7141028642654419},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6003091931343079},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.582055389881134},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4502526819705963},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4294130206108093},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4148104190826416},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.21701601147651672},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.10855203866958618},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3653781.3653797","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3653781.3653797","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the International Conference on Computer Vision and Deep Learning","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W2940129212","https://openalex.org/W2970913305","https://openalex.org/W2993206324","https://openalex.org/W3002219958","https://openalex.org/W3136119534","https://openalex.org/W3154823968","https://openalex.org/W3162665729","https://openalex.org/W3163257372","https://openalex.org/W3168997536","https://openalex.org/W3208246861","https://openalex.org/W4280543281","https://openalex.org/W4281873475","https://openalex.org/W4289816091","https://openalex.org/W4307090970","https://openalex.org/W4312798646","https://openalex.org/W4312906155","https://openalex.org/W4361229357","https://openalex.org/W4366153137","https://openalex.org/W4382657761","https://openalex.org/W4385517014"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W4297676672","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4281702477","https://openalex.org/W2490526372","https://openalex.org/W4376166922","https://openalex.org/W4378510483","https://openalex.org/W4389518428"],"abstract_inverted_index":{"Accurately":[0],"predicting":[1],"of":[2,6,11,23,29,33,52,74,106],"the":[3,12,20,30,75,82,85,97,125,130],"driving":[4,34,50,115],"behavior":[5,51,139],"adjacent":[7],"vehicles":[8],"is":[9,27,58,64,84,88],"one":[10,28],"things":[13],"that":[14,129],"need":[15],"to":[16,47,66,70,90,95],"be":[17],"done":[18],"before":[19],"control":[21],"decision":[22],"unmanned":[24],"driving,":[25],"and":[26,104,120],"key":[31],"factors":[32],"safety.":[35],"We":[36],"propose":[37],"a":[38,44,59,109],"CNN-Transformer":[39,131],"combined":[40],"model":[41,83,107,111,133],"based":[42],"on":[43],"data-driven":[45],"approach":[46],"accurately":[48,113],"predict":[49,114],"moving":[53],"vehicles.":[54,101],"The":[55],"first":[56],"module":[57,80],"Convolutional":[60],"Neural":[61],"Network,":[62],"which":[63,87],"used":[65,89],"extract":[67],"spatial":[68],"features":[69],"capture":[71,96],"behavioral":[72],"characteristics":[73,99],"vehicle.":[76],"In":[77],"addition,":[78],"another":[79],"in":[81,137],"Transformer,":[86],"process":[91],"time":[92],"series":[93],"data":[94],"interaction":[98],"between":[100],"Through":[102],"training":[103],"optimization":[105],"parameters,":[108],"deep":[110],"can":[112],"behavior,":[116],"while":[117],"reducing":[118],"overfitting":[119],"information":[121],"loss":[122],"problems.":[123],"Finally,":[124],"simulation":[126],"results":[127],"show":[128],"combination":[132],"has":[134],"excellent":[135],"performance":[136],"vehicle":[138],"prediction.":[140]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}