{"id":"https://openalex.org/W4400645949","doi":"https://doi.org/10.1109/iv55156.2024.10588864","title":"Eco-driving under localization uncertainty for connected vehicles on Urban roads: Data-driven approach and Experiment verification","display_name":"Eco-driving under localization uncertainty for connected vehicles on Urban roads: Data-driven approach and Experiment verification","publication_year":2024,"publication_date":"2024-06-02","ids":{"openalex":"https://openalex.org/W4400645949","doi":"https://doi.org/10.1109/iv55156.2024.10588864"},"language":"en","primary_location":{"id":"doi:10.1109/iv55156.2024.10588864","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv55156.2024.10588864","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 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/A5061734977","display_name":"Eunhyek Joa","orcid":"https://orcid.org/0000-0003-2973-2144"},"institutions":[{"id":"https://openalex.org/I4210159872","display_name":"Predictive Science (United States)","ror":"https://ror.org/05canvq15","country_code":"US","type":"company","lineage":["https://openalex.org/I4210159872"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Eunhyek Joa","raw_affiliation_strings":["University of California at Berkeley,Model Predictive Control Lab,Department of Mechanical Engineering"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California at Berkeley,Model Predictive Control Lab,Department of Mechanical Engineering","institution_ids":["https://openalex.org/I4210159872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087363423","display_name":"Eric Yongkeun Choi","orcid":null},"institutions":[{"id":"https://openalex.org/I4210159872","display_name":"Predictive Science (United States)","ror":"https://ror.org/05canvq15","country_code":"US","type":"company","lineage":["https://openalex.org/I4210159872"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Eric Yongkeun Choi","raw_affiliation_strings":["University of California at Berkeley,Model Predictive Control Lab,Department of Mechanical Engineering"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California at Berkeley,Model Predictive Control Lab,Department of Mechanical Engineering","institution_ids":["https://openalex.org/I4210159872"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025067440","display_name":"Francesco Borrelli","orcid":"https://orcid.org/0000-0001-8919-6430"},"institutions":[{"id":"https://openalex.org/I4210159872","display_name":"Predictive Science (United States)","ror":"https://ror.org/05canvq15","country_code":"US","type":"company","lineage":["https://openalex.org/I4210159872"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Francesco Borrelli","raw_affiliation_strings":["University of California at Berkeley,Model Predictive Control Lab,Department of Mechanical Engineering"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of California at Berkeley,Model Predictive Control Lab,Department of Mechanical Engineering","institution_ids":["https://openalex.org/I4210159872"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210159872"],"apc_list":null,"apc_paid":null,"fwci":0.9116,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.72379606,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"2904","last_page":"2909"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9801999926567078,"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":0.9801999926567078,"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"}},{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9785000085830688,"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/T10524","display_name":"Traffic control and management","score":0.9664999842643738,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5595377683639526},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4341079592704773},{"id":"https://openalex.org/keywords/automotive-engineering","display_name":"Automotive engineering","score":0.39022961258888245},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.3634423613548279},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.18613523244857788},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.07781612873077393}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5595377683639526},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4341079592704773},{"id":"https://openalex.org/C171146098","wikidata":"https://www.wikidata.org/wiki/Q124192","display_name":"Automotive engineering","level":1,"score":0.39022961258888245},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.3634423613548279},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.18613523244857788},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.07781612873077393}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iv55156.2024.10588864","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv55156.2024.10588864","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6499999761581421,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320306084","display_name":"U.S. Department of Energy","ror":"https://ror.org/01bj3aw27"},{"id":"https://openalex.org/F4320332815","display_name":"Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1506660872","https://openalex.org/W2076420009","https://openalex.org/W2221501280","https://openalex.org/W2258095608","https://openalex.org/W2749680651","https://openalex.org/W2797923482","https://openalex.org/W2914917317","https://openalex.org/W2972644282","https://openalex.org/W3001132159","https://openalex.org/W3011746747","https://openalex.org/W3129427813","https://openalex.org/W3160786793","https://openalex.org/W4206703688","https://openalex.org/W4210405825","https://openalex.org/W4224220201","https://openalex.org/W4225096168","https://openalex.org/W4245588349","https://openalex.org/W4313534757","https://openalex.org/W4315606037","https://openalex.org/W4382935759","https://openalex.org/W4385300831","https://openalex.org/W4400645949"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278","https://openalex.org/W4395014643"],"abstract_inverted_index":{"This":[0,48],"paper":[1],"addresses":[2],"the":[3,27,37,64,68,72,76,80,106,125,141],"eco-driving":[4],"problem":[5,38,93,138],"for":[6,101],"connected":[7],"vehicles":[8],"on":[9,24],"urban":[10],"roads,":[11],"considering":[12],"localization":[13],"uncertainty.":[14],"Eco-driving":[15],"is":[16,99],"defined":[17],"as":[18,147],"longitudinal":[19,130],"speed":[20,131,143],"planning":[21],"and":[22,55,71,98,139],"control":[23,45,137],"roads":[25],"with":[26],"presence":[28],"of":[29,32,108],"a":[30,41,52,95,134],"sequence":[31],"traffic":[33,82,87],"lights.":[34],"We":[35,104],"solve":[36],"by":[39,132,149],"using":[40,144],"data-driven":[42],"model":[43],"predictive":[44],"(MPC)":[46],"strategy.":[47],"approach":[49,110],"involves":[50],"learning":[51],"cost-to-go":[53,61],"function":[54,62],"constraints":[56,73],"from":[57,67],"state-input":[58],"data.":[59],"The":[60,89],"represents":[63],"remaining":[65],"energy-to-spend":[66],"given":[69],"state,":[70],"ensure":[74],"that":[75],"controlled":[77],"vehicle":[78,113,150],"passes":[79],"upcoming":[81],"light":[83],"timely":[84],"while":[85],"obeying":[86],"laws.":[88],"resulting":[90],"convex":[91],"optimization":[92],"has":[94],"short":[96],"horizon":[97],"amenable":[100],"real-time":[102],"implementations.":[103],"demonstrate":[105],"effectiveness":[107],"our":[109],"through":[111],"real-world":[112],"experiments.":[114,151],"Our":[115],"method":[116],"demonstrates":[117],"12%":[118],"improvement":[119],"in":[120],"energy":[121],"efficiency":[122],"compared":[123],"to":[124],"traditional":[126],"approaches,":[127],"which":[128],"plan":[129],"solving":[133],"long-horizon":[135],"optimal":[136],"track":[140],"planned":[142],"another":[145],"controller,":[146],"evidenced":[148]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
