{"id":"https://openalex.org/W4410298183","doi":"https://doi.org/10.1109/hri61500.2025.10973810","title":"Estimating the Trust of Humans in AI for Level 3 Autonomous Driving","display_name":"Estimating the Trust of Humans in AI for Level 3 Autonomous Driving","publication_year":2025,"publication_date":"2025-03-04","ids":{"openalex":"https://openalex.org/W4410298183","doi":"https://doi.org/10.1109/hri61500.2025.10973810"},"language":"en","primary_location":{"id":"doi:10.1109/hri61500.2025.10973810","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hri61500.2025.10973810","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 20th ACM/IEEE International Conference on Human-Robot Interaction (HRI)","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/A5108952509","display_name":"Sota Kaneko","orcid":"https://orcid.org/0000-0002-8340-7986"},"institutions":[{"id":"https://openalex.org/I184597095","display_name":"National Institute of Informatics","ror":"https://ror.org/04ksd4g47","country_code":"JP","type":"facility","lineage":["https://openalex.org/I1319490839","https://openalex.org/I184597095","https://openalex.org/I4210158934"]},{"id":"https://openalex.org/I200475212","display_name":"The Graduate University for Advanced Studies, SOKENDAI","ror":"https://ror.org/0516ah480","country_code":"JP","type":"education","lineage":["https://openalex.org/I200475212"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Sota Kaneko","raw_affiliation_strings":["The Graduate University for Advanced Studies, SOKENDAI National Institute of Informatics,Chiyoda-ku, Tokyo,Japan"],"affiliations":[{"raw_affiliation_string":"The Graduate University for Advanced Studies, SOKENDAI National Institute of Informatics,Chiyoda-ku, Tokyo,Japan","institution_ids":["https://openalex.org/I200475212","https://openalex.org/I184597095"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101954161","display_name":"Seiji Yamada","orcid":"https://orcid.org/0000-0002-5907-7382"},"institutions":[{"id":"https://openalex.org/I200475212","display_name":"The Graduate University for Advanced Studies, SOKENDAI","ror":"https://ror.org/0516ah480","country_code":"JP","type":"education","lineage":["https://openalex.org/I200475212"]},{"id":"https://openalex.org/I184597095","display_name":"National Institute of Informatics","ror":"https://ror.org/04ksd4g47","country_code":"JP","type":"facility","lineage":["https://openalex.org/I1319490839","https://openalex.org/I184597095","https://openalex.org/I4210158934"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Seiji Yamada","raw_affiliation_strings":["National Institute of Informatics, The Graduate University for Advanced Studies, SOKENDAI,Chiyoda-ku, Tokyo,Japan"],"affiliations":[{"raw_affiliation_string":"National Institute of Informatics, The Graduate University for Advanced Studies, SOKENDAI,Chiyoda-ku, Tokyo,Japan","institution_ids":["https://openalex.org/I184597095","https://openalex.org/I200475212"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5108952509"],"corresponding_institution_ids":["https://openalex.org/I184597095","https://openalex.org/I200475212"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.05105089,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1389","last_page":"1392"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9563000202178955,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9563000202178955,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10883","display_name":"Ethics and Social Impacts of AI","score":0.932699978351593,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9046000242233276,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5966148972511292},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3298913836479187}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5966148972511292},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3298913836479187}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/hri61500.2025.10973810","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hri61500.2025.10973810","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 20th ACM/IEEE International Conference on Human-Robot Interaction (HRI)","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":0,"referenced_works":[],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Trust":[0,23],"in":[1,13,105,127],"AI":[2,12],"is":[3,55,62,69,103,114],"crucial":[4],"for":[5,80,116],"a":[6,77],"cooperative":[7],"relationship":[8],"between":[9,101],"humans":[10,37,67],"and":[11,48,68,90,109],"systems":[14],"utilizing":[15],"this":[16],"technology,":[17],"such":[18,45],"as":[19,46],"autonomous":[20,129],"driving":[21,130],"systems.":[22],"facilitates":[24],"the":[25,110,140],"appropriate":[26],"utilization":[27],"of":[28,66],"these":[29],"systems,":[30],"thereby":[31],"optimizing":[32],"their":[33],"potential":[34],"benefits.":[35],"If":[36],"over-trust":[38],"or":[39],"under-trust":[40],"an":[41,63,106,128,144],"AI,":[42],"serious":[43],"problems":[44],"misuse":[47],"accidents":[49],"occur.":[50],"To":[51],"prevent":[52],"over/under-trust,":[53],"it":[54],"necessary":[56],"to":[57,71],"estimate":[58],"trust.":[59],"However,":[60],"trust":[61,81],"internal":[64],"state":[65],"hard":[70],"observe":[72],"directly.":[73],"Therefore,":[74],"we":[75],"propose":[76],"estimation":[78],"model":[79],"using":[82],"dynamic":[83],"structure":[84],"equation":[85],"modeling,":[86],"which":[87,98],"extends":[88],"SEM":[89],"can":[91],"handle":[92],"time-series":[93],"data.":[94],"A":[95],"path":[96,112,118],"diagram,":[97],"shows":[99],"causalities":[100],"variables,":[102],"developed":[104],"exploratory":[107],"way":[108],"resultant":[111],"diagram":[113],"optimized":[115],"effective":[117],"structures.":[119],"Over/under-trust":[120],"was":[121],"estimated":[122],"with":[123],"99":[124],"%":[125],"accuracy":[126],"task.":[131],"These":[132],"results":[133],"show":[134],"that":[135],"our":[136],"proposed":[137],"method":[138,142],"outperformed":[139],"conventional":[141],"including":[143],"auto":[145],"regression":[146],"family.":[147]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
