{"id":"https://openalex.org/W2914237600","doi":"https://doi.org/10.1109/ssci.2018.8628631","title":"An Autonomous Driving Experience Platform with Learning-Based Functions","display_name":"An Autonomous Driving Experience Platform with Learning-Based Functions","publication_year":2018,"publication_date":"2018-11-01","ids":{"openalex":"https://openalex.org/W2914237600","doi":"https://doi.org/10.1109/ssci.2018.8628631","mag":"2914237600"},"language":"en","primary_location":{"id":"doi:10.1109/ssci.2018.8628631","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci.2018.8628631","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","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/A5108049281","display_name":"Dong Li","orcid":"https://orcid.org/0000-0002-2692-9091"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Dong Li","raw_affiliation_strings":["University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100624298","display_name":"Dongbin Zhao","orcid":"https://orcid.org/0000-0001-8218-9633"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dongbin Zhao","raw_affiliation_strings":["University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049454999","display_name":"Qichao Zhang","orcid":"https://orcid.org/0000-0001-9747-391X"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qichao Zhang","raw_affiliation_strings":["University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080193690","display_name":"Yuanheng Zhu","orcid":"https://orcid.org/0000-0001-5384-423X"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanheng Zhu","raw_affiliation_strings":["University of Chinese Academy of Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5108049281"],"corresponding_institution_ids":["https://openalex.org/I4210165038"],"apc_list":null,"apc_paid":null,"fwci":0.1475,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.56722268,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"1","issue":null,"first_page":"1174","last_page":"1179"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9998000264167786,"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":0.9998000264167786,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9993000030517578,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9950000047683716,"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/beijing","display_name":"Beijing","score":0.6844708323478699},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6016004085540771},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5672606229782104},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5659096240997314},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5105770826339722},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.48474547266960144},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4654584228992462},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.358743280172348},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.32923394441604614},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.12326425313949585}],"concepts":[{"id":"https://openalex.org/C2778304055","wikidata":"https://www.wikidata.org/wiki/Q657474","display_name":"Beijing","level":3,"score":0.6844708323478699},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6016004085540771},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5672606229782104},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5659096240997314},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5105770826339722},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.48474547266960144},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4654584228992462},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.358743280172348},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.32923394441604614},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.12326425313949585},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C191935318","wikidata":"https://www.wikidata.org/wiki/Q148","display_name":"China","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ssci.2018.8628631","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci.2018.8628631","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7200000286102295}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W2031031248","https://openalex.org/W2048257477","https://openalex.org/W2064451896","https://openalex.org/W2078598284","https://openalex.org/W2107634464","https://openalex.org/W2107656445","https://openalex.org/W2113223387","https://openalex.org/W2132266610","https://openalex.org/W2145339207","https://openalex.org/W2161005943","https://openalex.org/W2162390532","https://openalex.org/W2164598857","https://openalex.org/W2165150801","https://openalex.org/W2524771588","https://openalex.org/W2550190059","https://openalex.org/W2606548817","https://openalex.org/W2727840223","https://openalex.org/W2742332513","https://openalex.org/W2793780249","https://openalex.org/W2962887844","https://openalex.org/W2963037989","https://openalex.org/W4302570325","https://openalex.org/W6657952867","https://openalex.org/W6683862498","https://openalex.org/W6684205842","https://openalex.org/W6749949824"],"related_works":["https://openalex.org/W2015747722","https://openalex.org/W2362050182","https://openalex.org/W2382418233","https://openalex.org/W2369897927","https://openalex.org/W3031731056","https://openalex.org/W4293167957","https://openalex.org/W2969228573","https://openalex.org/W2963690996","https://openalex.org/W4292830139","https://openalex.org/W4319309705"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"an":[3,43],"autonomous":[4,17],"driving":[5,18],"experience":[6],"platform":[7,21,107],"for":[8],"the":[9,13,51,54,57,64,80,86,91,94,100,103],"purpose":[10],"of":[11,16,102],"improving":[12],"social":[14],"acceptance":[15],"technologies.":[19],"The":[20,38,70,97,106],"includes":[22],"two":[23],"modules:":[24],"a":[25,32],"dangerous":[26,58],"object":[27,44],"detection":[28,45],"(DOD)":[29],"module":[30,40],"and":[31,67,84,114],"lane":[33,95],"keeping":[34],"assist":[35],"(LKA)":[36],"module.":[37],"DOD":[39],"first":[41],"employs":[42],"convolutional":[46],"neural":[47],"network":[48],"to":[49,89],"locate":[50],"vehicle":[52,92],"on":[53,63,76],"image,":[55],"then":[56],"level":[59],"is":[60,74,108],"determined":[61],"based":[62,75],"corresponding":[65],"location":[66],"geometrical":[68],"relationship.":[69],"LKA":[71],"module,":[72],"which":[73],"reinforcement":[77],"learning":[78],"inputs":[79],"physical":[81],"sensor":[82],"measurements":[83],"outputs":[85],"steering":[87],"command":[88],"control":[90],"in":[93,110],"center.":[96],"experiments":[98],"validate":[99],"effectiveness":[101],"proposed":[104],"methods.":[105],"exhibited":[109],"2018":[111],"Beijing":[112],"science":[113],"technology":[115],"week":[116],"<sup":[117],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[118],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sup>":[119],".":[120]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
