{"id":"https://openalex.org/W3119033000","doi":"https://doi.org/10.1109/iv47402.2020.9304566","title":"Optimal Control of Urban Intersection Scheduling for Connected Automated Vehicles","display_name":"Optimal Control of Urban Intersection Scheduling for Connected Automated Vehicles","publication_year":2020,"publication_date":"2020-10-19","ids":{"openalex":"https://openalex.org/W3119033000","doi":"https://doi.org/10.1109/iv47402.2020.9304566","mag":"3119033000"},"language":"en","primary_location":{"id":"doi:10.1109/iv47402.2020.9304566","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv47402.2020.9304566","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Intelligent Vehicles Symposium (IV)","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/A5110927146","display_name":"Shenghao Jiang","orcid":null},"institutions":[{"id":"https://openalex.org/I136199984","display_name":"Harvard University","ror":"https://ror.org/03vek6s52","country_code":"US","type":"education","lineage":["https://openalex.org/I136199984"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Shenghao Jiang","raw_affiliation_strings":["School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA","institution_ids":["https://openalex.org/I136199984"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5110927146"],"corresponding_institution_ids":["https://openalex.org/I136199984"],"apc_list":null,"apc_paid":null,"fwci":0.2942,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.58098071,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"524","last_page":"531"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10524","display_name":"Traffic control and management","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10524","display_name":"Traffic control and management","score":0.9998999834060669,"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"}},{"id":"https://openalex.org/T10761","display_name":"Vehicular Ad Hoc Networks (VANETs)","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9991000294685364,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7242652177810669},{"id":"https://openalex.org/keywords/scheduling","display_name":"Scheduling (production processes)","score":0.6437609195709229},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.6322612762451172},{"id":"https://openalex.org/keywords/schedule","display_name":"Schedule","score":0.6177768111228943},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.40603211522102356},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.38509052991867065},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.3551740050315857},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.144936203956604},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.09058091044425964},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09029847383499146}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7242652177810669},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.6437609195709229},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.6322612762451172},{"id":"https://openalex.org/C68387754","wikidata":"https://www.wikidata.org/wiki/Q7271585","display_name":"Schedule","level":2,"score":0.6177768111228943},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.40603211522102356},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.38509052991867065},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3551740050315857},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.144936203956604},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.09058091044425964},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09029847383499146},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iv47402.2020.9304566","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv47402.2020.9304566","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.7099999785423279,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1757796397","https://openalex.org/W1969806274","https://openalex.org/W1998717782","https://openalex.org/W2009802855","https://openalex.org/W2107338474","https://openalex.org/W2109199369","https://openalex.org/W2510875290","https://openalex.org/W2782493803","https://openalex.org/W2791607082","https://openalex.org/W2805498560","https://openalex.org/W2887843538","https://openalex.org/W2888069589","https://openalex.org/W2913775587","https://openalex.org/W2942662862","https://openalex.org/W2962700127","https://openalex.org/W2968049026","https://openalex.org/W2980820698","https://openalex.org/W3003939524","https://openalex.org/W4298857966","https://openalex.org/W6637967152","https://openalex.org/W6652950373","https://openalex.org/W6724322699"],"related_works":["https://openalex.org/W2348909947","https://openalex.org/W4292672442","https://openalex.org/W2362101859","https://openalex.org/W2791431590","https://openalex.org/W2941610985","https://openalex.org/W4235810826","https://openalex.org/W3000105423","https://openalex.org/W2350688482","https://openalex.org/W2122135111","https://openalex.org/W2053575972"],"abstract_inverted_index":{"We":[0],"propose":[1,74],"a":[2,24,159],"novel":[3],"urban":[4,163],"congestion-aware":[5],"intersection":[6,44,140],"scheduling":[7,98,148],"model":[8,27],"based":[9,111,157],"on":[10,158],"vehicle":[11],"to":[12,41,78,95,118,139],"infrastructure":[13],"communication":[14],"(V2I)":[15],"for":[16,127,145],"automated":[17],"and":[18,32,47,53,107,123,179],"connected":[19],"vehicles.":[20,129],"In":[21,39],"this":[22,153],"model,":[23],"combinational":[25],"optimized":[26,71],"which":[28,100],"combines":[29],"passing":[30],"order":[31,40],"vehicular":[33],"motion":[34,112],"control":[35],"together":[36],"is":[37,57,93,116,155],"proposed.":[38],"resolve":[42,96],"the":[43,60,65,81,85,97,102,120],"conflict":[45,55],"issue":[46],"improve":[48],"traffic":[49,181],"capacity,":[50],"driving":[51,103],"tube":[52],"potential":[54],"matrix":[56],"applied":[58,94],"in":[59,84,173],"schedule":[61],"optimization":[62],"model.":[63],"Take":[64],"global":[66],"average":[67,176],"waiting":[68,177],"time":[69,132,147,178],"as":[70],"object,":[72],"we":[73],"state":[75],"encoding":[76],"approach":[77],"collect":[79],"all":[80],"vehicle's":[82],"information":[83],"intersection.":[86],"Then":[87],"Deep":[88],"Q":[89],"Network":[90],"(DQN)":[91],"method":[92],"problem,":[99],"outputs":[101],"tubes":[104],"enable":[105],"vector":[106],"subsequently":[108],"7th":[109],"polynomial":[110],"planning":[113,115],"trajectory":[114,126],"exploited":[117],"generate":[119],"most":[121,124],"comfortable":[122],"efficient":[125],"active":[128],"The":[130,150],"optimal":[131],"cost":[133],"profile":[134],"will":[135],"be":[136],"feed":[137],"back":[138],"manager":[141],"via":[142],"V2I":[143],"channel":[144],"next":[146],"decision.":[149],"performance":[151],"of":[152,175],"framework":[154,169],"evaluated":[156],"typical":[160],"Chinese":[161],"complicated":[162],"scenario":[164],"with":[165],"extensive":[166],"simulation,":[167],"our":[168],"achieves":[170],"encouraging":[171],"results":[172],"terms":[174],"peak":[180],"throughput.":[182]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
