{"id":"https://openalex.org/W2155338102","doi":"https://doi.org/10.1109/ivs.2011.5940541","title":"Intelligent headlight control using learning-based approaches","display_name":"Intelligent headlight control using learning-based approaches","publication_year":2011,"publication_date":"2011-06-01","ids":{"openalex":"https://openalex.org/W2155338102","doi":"https://doi.org/10.1109/ivs.2011.5940541","mag":"2155338102"},"language":"en","primary_location":{"id":"doi:10.1109/ivs.2011.5940541","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2011.5940541","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2011 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5106406655","display_name":"Ying Li","orcid":"https://orcid.org/0009-0004-1669-1878"},"institutions":[{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ying Li","raw_affiliation_strings":["IBM Thomas J. Watson Research Center, Hawthorne, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Thomas J. Watson Research Center, Hawthorne, NY, USA","institution_ids":["https://openalex.org/I4210114115"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039564215","display_name":"Norman Haas","orcid":null},"institutions":[{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Norman Haas","raw_affiliation_strings":["IBM Thomas J. Watson Research Center, Hawthorne, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Thomas J. Watson Research Center, Hawthorne, NY, USA","institution_ids":["https://openalex.org/I4210114115"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5078542580","display_name":"Sharath Pankanti","orcid":"https://orcid.org/0000-0001-6770-9899"},"institutions":[{"id":"https://openalex.org/I4210114115","display_name":"IBM Research - Thomas J. Watson Research Center","ror":"https://ror.org/0265w5591","country_code":"US","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sharath Pankanti","raw_affiliation_strings":["IBM Thomas J. Watson Research Center, Hawthorne, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IBM Thomas J. Watson Research Center, Hawthorne, NY, USA","institution_ids":["https://openalex.org/I4210114115"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210114115"],"apc_list":null,"apc_paid":null,"fwci":0.7134,"has_fulltext":false,"cited_by_count":24,"citation_normalized_percentile":{"value":0.72418022,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"722","last_page":"727"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.9932000041007996,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11019","display_name":"Image Enhancement Techniques","score":0.9932000041007996,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11963","display_name":"Impact of Light on Environment and Health","score":0.9883999824523926,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9851999878883362,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.7590174674987793},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7310703992843628},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5807317495346069},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5638836622238159},{"id":"https://openalex.org/keywords/adaboost","display_name":"AdaBoost","score":0.49485987424850464},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46367523074150085},{"id":"https://openalex.org/keywords/overtaking","display_name":"Overtaking","score":0.44889241456985474},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.21536895632743835}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7590174674987793},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7310703992843628},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5807317495346069},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5638836622238159},{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.49485987424850464},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46367523074150085},{"id":"https://openalex.org/C2778448659","wikidata":"https://www.wikidata.org/wiki/Q1931051","display_name":"Overtaking","level":2,"score":0.44889241456985474},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.21536895632743835},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ivs.2011.5940541","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2011.5940541","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2011 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.8399999737739563}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W1968969471","https://openalex.org/W2147320665","https://openalex.org/W2153635508","https://openalex.org/W6681747429"],"related_works":["https://openalex.org/W3084163143","https://openalex.org/W3198453306","https://openalex.org/W3081587374","https://openalex.org/W591161629","https://openalex.org/W2294880310","https://openalex.org/W3004478217","https://openalex.org/W3208192858","https://openalex.org/W1998625354","https://openalex.org/W2102544652","https://openalex.org/W2618142536"],"abstract_inverted_index":{"This":[0],"paper":[1],"describes":[2],"our":[3],"recent":[4],"work":[5],"on":[6,39],"developing":[7],"an":[8],"intelligent":[9],"headlight":[10],"control":[11,24],"system":[12,20,94],"using":[13],"machine":[14,58,64],"learning-based":[15,59],"approaches.":[16,113],"Specifically,":[17],"such":[18],"a":[19,25,35,55],"aims":[21],"to":[22,71,103],"automatically":[23],"vehicle's":[26],"beam":[27,30],"state":[28],"(high":[29],"or":[31],"low":[32],"beam)":[33],"during":[34],"night-time":[36],"drive":[37],"based":[38],"the":[40,51,91,105,110,123,130],"detection":[41],"of":[42,77,109],"oncoming/overtaking/leading":[43],"traffics":[44],"as":[45,47,80,82],"well":[46,81],"urban":[48],"areas":[49],"from":[50],"videos":[52],"captured":[53],"by":[54],"camera.":[56],"Two":[57],"approaches,":[60],"namely,":[61],"support":[62],"vector":[63],"(SVM)":[65],"and":[66,101,107],"AdaBoost,":[67],"have":[68],"been":[69,96],"applied":[70],"accomplish":[72],"this":[73],"task.":[74],"The":[75,93],"architect":[76],"each":[78],"approach,":[79],"its":[83],"detailed":[84,115],"processing":[85],"modules,":[86],"will":[87,126],"be":[88,127],"elaborated":[89],"in":[90],"paper.":[92],"has":[95],"extensively":[97],"tested":[98],"both":[99],"online":[100],"offline":[102],"validate":[104],"robustness":[106],"effectiveness":[108],"two":[111,124],"proposed":[112],"A":[114],"performance":[116],"study":[117],"along":[118],"with":[119],"some":[120],"comparisons":[121],"between":[122],"approaches":[125],"reported":[128],"at":[129],"end.":[131]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2023,"cited_by_count":3},{"year":2021,"cited_by_count":3},{"year":2019,"cited_by_count":5},{"year":2017,"cited_by_count":3},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}