{"id":"https://openalex.org/W4402475088","doi":"https://doi.org/10.1109/tvt.2024.3457917","title":"A Self-Selection Personalized Lane-Changing Trajectory Prediction Approach Through a Hybrid Deep Learning Model","display_name":"A Self-Selection Personalized Lane-Changing Trajectory Prediction Approach Through a Hybrid Deep Learning Model","publication_year":2024,"publication_date":"2024-09-12","ids":{"openalex":"https://openalex.org/W4402475088","doi":"https://doi.org/10.1109/tvt.2024.3457917"},"language":"en","primary_location":{"id":"doi:10.1109/tvt.2024.3457917","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2024.3457917","pdf_url":null,"source":{"id":"https://openalex.org/S10936095","display_name":"IEEE Transactions on Vehicular Technology","issn_l":"0018-9545","issn":["0018-9545","1939-9359"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Vehicular Technology","raw_type":"journal-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/A5053001518","display_name":"Zhao Li","orcid":"https://orcid.org/0000-0003-1530-8115"},"institutions":[{"id":"https://openalex.org/I25355098","display_name":"Chang'an University","ror":"https://ror.org/05mxya461","country_code":"CN","type":"education","lineage":["https://openalex.org/I25355098"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhao Li","raw_affiliation_strings":["School of Automobile, Chang'an University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0003-1530-8115","affiliations":[{"raw_affiliation_string":"School of Automobile, Chang'an University, Xi'an, China","institution_ids":["https://openalex.org/I25355098"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073443361","display_name":"Xia Zhao","orcid":"https://orcid.org/0000-0001-6683-3652"},"institutions":[{"id":"https://openalex.org/I115592961","display_name":"Jiangsu University","ror":"https://ror.org/03jc41j30","country_code":"CN","type":"education","lineage":["https://openalex.org/I115592961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xia Zhao","raw_affiliation_strings":["Automotive Engineering Research Institute, Jiangsu University, Zhenjiang, China"],"raw_orcid":"https://orcid.org/0000-0001-6683-3652","affiliations":[{"raw_affiliation_string":"Automotive Engineering Research Institute, Jiangsu University, Zhenjiang, China","institution_ids":["https://openalex.org/I115592961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029775270","display_name":"Chen Zhao","orcid":"https://orcid.org/0000-0002-3069-4926"},"institutions":[{"id":"https://openalex.org/I25355098","display_name":"Chang'an University","ror":"https://ror.org/05mxya461","country_code":"CN","type":"education","lineage":["https://openalex.org/I25355098"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chen Zhao","raw_affiliation_strings":["School of Automobile, Chang'an University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0002-3069-4926","affiliations":[{"raw_affiliation_string":"School of Automobile, Chang'an University, Xi'an, China","institution_ids":["https://openalex.org/I25355098"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052998152","display_name":"Yongtao Liu","orcid":"https://orcid.org/0000-0003-3643-4276"},"institutions":[{"id":"https://openalex.org/I25355098","display_name":"Chang'an University","ror":"https://ror.org/05mxya461","country_code":"CN","type":"education","lineage":["https://openalex.org/I25355098"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongtao Liu","raw_affiliation_strings":["School of Automobile, Chang'an University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0003-3643-4276","affiliations":[{"raw_affiliation_string":"School of Automobile, Chang'an University, Xi'an, China","institution_ids":["https://openalex.org/I25355098"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5042397552","display_name":"Chang Wang","orcid":"https://orcid.org/0000-0003-3531-1215"},"institutions":[{"id":"https://openalex.org/I25355098","display_name":"Chang'an University","ror":"https://ror.org/05mxya461","country_code":"CN","type":"education","lineage":["https://openalex.org/I25355098"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chang Wang","raw_affiliation_strings":["School of Automobile, Chang'an University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0003-3531-1215","affiliations":[{"raw_affiliation_string":"School of Automobile, Chang'an University, Xi'an, China","institution_ids":["https://openalex.org/I25355098"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4259,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.6115699,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":97},"biblio":{"volume":"74","issue":"1","first_page":"332","last_page":"347"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9976999759674072,"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":0.9976999759674072,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9735000133514404,"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/T10524","display_name":"Traffic control and management","score":0.9573000073432922,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/trajectory","display_name":"Trajectory","score":0.7214251756668091},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6587961912155151},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6164058446884155},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.6068953275680542},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5885903239250183},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4225786328315735}],"concepts":[{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.7214251756668091},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6587961912155151},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6164058446884155},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.6068953275680542},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5885903239250183},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4225786328315735},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/tvt.2024.3457917","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvt.2024.3457917","pdf_url":null,"source":{"id":"https://openalex.org/S10936095","display_name":"IEEE Transactions on Vehicular Technology","issn_l":"0018-9545","issn":["0018-9545","1939-9359"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Vehicular Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7668589249","display_name":null,"funder_award_id":"2023-JC-YB-596","funder_id":"https://openalex.org/F4320324173","funder_display_name":"Natural Science Foundation of Shaanxi Province"}],"funders":[{"id":"https://openalex.org/F4320324173","display_name":"Natural Science Foundation of Shaanxi Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W1988382218","https://openalex.org/W2067261106","https://openalex.org/W2133747588","https://openalex.org/W2745090846","https://openalex.org/W2767029132","https://openalex.org/W2889556029","https://openalex.org/W2896760409","https://openalex.org/W2903788633","https://openalex.org/W2909419488","https://openalex.org/W2913367279","https://openalex.org/W2938440964","https://openalex.org/W2954631042","https://openalex.org/W2958960077","https://openalex.org/W2963311488","https://openalex.org/W2963319512","https://openalex.org/W2967183172","https://openalex.org/W2977340052","https://openalex.org/W2996287921","https://openalex.org/W2997958396","https://openalex.org/W3016478929","https://openalex.org/W3022220688","https://openalex.org/W3045140757","https://openalex.org/W3096351988","https://openalex.org/W3128196514","https://openalex.org/W3132875902","https://openalex.org/W3159416998","https://openalex.org/W3163257372","https://openalex.org/W3164340576","https://openalex.org/W3186450693","https://openalex.org/W3194047217","https://openalex.org/W3197793043","https://openalex.org/W3206156324","https://openalex.org/W3208372618","https://openalex.org/W3214003911","https://openalex.org/W4205456267","https://openalex.org/W4213446430","https://openalex.org/W4214895168","https://openalex.org/W4226239849","https://openalex.org/W4283823813","https://openalex.org/W4285205265","https://openalex.org/W4287888056","https://openalex.org/W4289823407","https://openalex.org/W4293812004","https://openalex.org/W4295392623","https://openalex.org/W4296312011","https://openalex.org/W4309734258","https://openalex.org/W4312234505","https://openalex.org/W4319303149","https://openalex.org/W4319767771","https://openalex.org/W4320712846","https://openalex.org/W4323338412","https://openalex.org/W4386400738","https://openalex.org/W4394604574"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W2961085424","https://openalex.org/W3215138031","https://openalex.org/W4306674287","https://openalex.org/W3009238340","https://openalex.org/W4321369474","https://openalex.org/W4360585206","https://openalex.org/W4285208911","https://openalex.org/W3046775127","https://openalex.org/W3082895349"],"abstract_inverted_index":{"The":[0,110,175,183],"difference":[1],"between":[2],"the":[3,7,10,20,47,50,61,65,81,90,94,98,107,114,123,128,131,137,143,146,173,188,195,208],"desired":[4],"trajectory":[5,13,33,154],"of":[6,14,19,49,54,64,89,97,127,142,194,211],"drivers":[8],"and":[9,85,92,103,113,130,163,167,204,214],"lane":[11,31,51,124,138,152,158],"change":[12,32,52,125,139,153,159],"autonomous":[15,55],"vehicles":[16,91],"is":[17,76,133,149,178],"one":[18],"crucial":[21],"factors":[22],"that":[23,187],"limit":[24],"their":[25],"development.":[26],"This":[27],"paper":[28],"proposes":[29],"a":[30,67,134,219],"prediction":[34,155,222],"framework,":[35],"referred":[36],"to":[37,45,59,79,106,121,172],"as":[38,170],"LCT-DPP,":[39],"which":[40,217],"incorporates":[41],"driver":[42,99],"personality":[43],"parameters":[44,88],"improve":[46],"acceptance":[48],"trajectories":[53,84],"vehicles.":[56],"In":[57],"order":[58],"identify":[60],"lane-changing":[62,83,95],"styles":[63,96],"drivers,":[66],"clustering":[68,108],"algorithm":[69],"Autoencoder":[70],"(AE)":[71],"-Gaussian":[72],"Mixture":[73],"Model":[74],"(GMM)":[75],"first":[77],"proposed":[78,176],"cluster":[80],"historical":[82],"vehicle":[86],"interaction":[87],"classify":[93],"into":[100],"aggressive,":[101],"normal,":[102],"conservative":[104],"according":[105],"results.":[109],"encoder":[111],"module":[112],"fully":[115],"connected":[116],"layer":[117],"are":[118,200],"then":[119,179],"used":[120],"predict":[122],"style":[126,140,160],"driver,":[129],"output":[132],"vector":[135],"holding":[136],"features":[141,166],"driver.":[144],"Finally,":[145],"encoder-decoder":[147,198],"model":[148,177,199],"developed":[150],"for":[151,207],"by":[156],"fusing":[157],"feature":[161],"vectors":[162],"time":[164,209],"series":[165],"using":[168],"them":[169],"inputs":[171],"model.":[174],"validated":[180],"through":[181],"experiments.":[182],"obtained":[184],"results":[185],"show":[186],"root":[189],"mean":[190],"square":[191],"errors":[192],"(RMSEs)":[193],"TCN-LSTM-Attention":[196],"based":[197],"respectively":[201],"0.312,":[202],"0.568,":[203],"0.734":[205],"m":[206],"intervals":[210],"1,":[212],"2,":[213],"3":[215],"s,":[216],"indicates":[218],"very":[220],"high":[221],"accuracy.":[223]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}