{"id":"https://openalex.org/W4403977171","doi":"https://doi.org/10.1109/icite59717.2023.10733903","title":"Driving Fixation Prediction Model via a Hybrid Encoding-Decoding Network with Transformer","display_name":"Driving Fixation Prediction Model via a Hybrid Encoding-Decoding Network with Transformer","publication_year":2023,"publication_date":"2023-10-28","ids":{"openalex":"https://openalex.org/W4403977171","doi":"https://doi.org/10.1109/icite59717.2023.10733903"},"language":"en","primary_location":{"id":"doi:10.1109/icite59717.2023.10733903","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icite59717.2023.10733903","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 8th International Conference on Intelligent Transportation Engineering (ICITE)","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/A5076077777","display_name":"Sichen Wang","orcid":"https://orcid.org/0009-0005-7138-7622"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Sichen Wang","raw_affiliation_strings":["Southwest Jiaotong University,Chengdu,China"],"affiliations":[{"raw_affiliation_string":"Southwest Jiaotong University,Chengdu,China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023026687","display_name":"Zifeng Li","orcid":"https://orcid.org/0000-0002-0625-1578"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Zifeng Li","raw_affiliation_strings":["Nanyang Technological University,Singapore"],"affiliations":[{"raw_affiliation_string":"Nanyang Technological University,Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090119778","display_name":"Shihui Ji","orcid":null},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shihui Ji","raw_affiliation_strings":["Southwest Jiaotong University,Chengdu,China"],"affiliations":[{"raw_affiliation_string":"Southwest Jiaotong University,Chengdu,China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075718768","display_name":"Pengcheng Du","orcid":"https://orcid.org/0009-0002-2855-9474"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengcheng Du","raw_affiliation_strings":["Southwest Jiaotong University,Chengdu,China"],"affiliations":[{"raw_affiliation_string":"Southwest Jiaotong University,Chengdu,China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031400194","display_name":"Tao Deng","orcid":"https://orcid.org/0000-0001-5094-5879"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Deng","raw_affiliation_strings":["Southwest Jiaotong University,Chengdu,China"],"affiliations":[{"raw_affiliation_string":"Southwest Jiaotong University,Chengdu,China","institution_ids":["https://openalex.org/I4800084"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5076077777"],"corresponding_institution_ids":["https://openalex.org/I4800084"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.25970367,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"401","last_page":"406"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9681000113487244,"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":0.9681000113487244,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.8240112066268921},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.688662052154541},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.555005669593811},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.5438836216926575},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.37590670585632324},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3297712504863739},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.29045405983924866},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.14768126606941223},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.1282581090927124},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.10730493068695068}],"concepts":[{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.8240112066268921},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.688662052154541},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.555005669593811},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.5438836216926575},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37590670585632324},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3297712504863739},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.29045405983924866},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14768126606941223},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.1282581090927124},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.10730493068695068}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icite59717.2023.10733903","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icite59717.2023.10733903","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 8th International Conference on Intelligent Transportation Engineering (ICITE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7200000286102295,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[{"id":"https://openalex.org/G4565923473","display_name":null,"funder_award_id":"62106208","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7222901146","display_name":null,"funder_award_id":"2021TQ0272,2021M702715","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1510835000","https://openalex.org/W1517086206","https://openalex.org/W1559169059","https://openalex.org/W1901129140","https://openalex.org/W2032007016","https://openalex.org/W2037328649","https://openalex.org/W2055111849","https://openalex.org/W2152233525","https://openalex.org/W2169949291","https://openalex.org/W2192271525","https://openalex.org/W2474210745","https://openalex.org/W2798122215","https://openalex.org/W2887806157","https://openalex.org/W2947918549","https://openalex.org/W2963503775","https://openalex.org/W2963828885","https://openalex.org/W2986131415","https://openalex.org/W3011154664","https://openalex.org/W3118519864","https://openalex.org/W3166547122","https://openalex.org/W4206711354","https://openalex.org/W6750469568","https://openalex.org/W6764953915","https://openalex.org/W6780226713"],"related_works":["https://openalex.org/W2161474341","https://openalex.org/W2368824897","https://openalex.org/W1508050556","https://openalex.org/W1910862367","https://openalex.org/W2379365082","https://openalex.org/W2370747590","https://openalex.org/W2030109976","https://openalex.org/W2369260257","https://openalex.org/W2389120450","https://openalex.org/W55249799"],"abstract_inverted_index":{"Although":[0],"visual":[1,39,49,69,182],"attention":[2,24,41,83,89],"mechanisms":[3,84],"have":[4],"been":[5],"developed":[6],"for":[7,71,85,143],"a":[8,30,95,117],"long":[9],"time":[10],"in":[11,26,154,168],"the":[12,48,121,124,137,149,160,187],"field":[13],"of":[14,47,68,106,123,140,152,164],"computer":[15],"vision,":[16],"there":[17],"is":[18,42],"not":[19],"much":[20],"research":[21],"on":[22,186],"driver":[23,97],"patterns":[25],"driving":[27,32,75,107],"scenarios.":[28],"In":[29],"complex":[31],"environment":[33],"with":[34],"dynamic":[35],"multidimensional":[36],"information,":[37],"drivers'":[38,82],"selective":[40],"an":[43],"important":[44],"neural":[45,113,161],"mechanism":[46],"system":[50],"to":[51,131],"extract":[52],"key":[53],"scene":[54,108],"information":[55],"and":[56,66,77,115,174],"filter":[57],"out":[58],"redundant":[59],"information.":[60],"This":[61,158],"provides":[62],"useful":[63],"theoretical":[64],"basis":[65],"techniques":[67],"perception":[70],"future":[72],"intelligent":[73],"vehicles,":[74],"training":[76],"ADAS.":[78],"To":[79],"incorporate":[80],"human":[81],"understanding":[86],"their":[87],"latent":[88],"allocation":[90],"patterns,":[91],"this":[92],"paper":[93],"proposes":[94],"transformer-based":[96],"fixation":[98],"prediction":[99],"model,":[100],"HEDTs.":[101],"HEDTs":[102,135,178],"extracts":[103],"low-level":[104],"features":[105,167],"images":[109],"through":[110],"multi-layer":[111],"convolutional":[112,141],"networks":[114],"introduces":[116],"transformer":[118],"layer":[119],"at":[120],"end":[122],"feature":[125],"extraction":[126,163],"network.":[127],"By":[128],"using":[129],"self-attention":[130],"encode":[132],"high-level":[133,165],"features,":[134],"maintains":[136],"modeling":[138,155],"ability":[139,151],"layers":[142],"neighboring":[144],"pixel":[145],"relationships":[146],"while":[147],"leveraging":[148],"excellent":[150],"transformers":[153],"long-distance":[156],"dependencies.":[157],"improves":[159],"network's":[162],"semantic":[166],"images.":[169],"With":[170],"fewer":[171],"model":[172],"parameters":[173],"faster":[175],"inference":[176],"speed,":[177],"outperforms":[179],"many":[180],"advanced":[181],"saliency":[183],"detection":[184],"models":[185],"test":[188],"dataset.":[189]},"counts_by_year":[],"updated_date":"2025-12-27T23:08:20.325037","created_date":"2025-10-10T00:00:00"}
