{"id":"https://openalex.org/W4416259191","doi":"https://doi.org/10.48550/arxiv.2509.13386","title":"VEGA: Electric Vehicle Navigation Agent via Physics-Informed Neural Operator and Proximal Policy Optimization","display_name":"VEGA: Electric Vehicle Navigation Agent via Physics-Informed Neural Operator and Proximal Policy Optimization","publication_year":2025,"publication_date":"2025-09-16","ids":{"openalex":"https://openalex.org/W4416259191","doi":"https://doi.org/10.48550/arxiv.2509.13386"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2509.13386","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.13386","pdf_url":"https://arxiv.org/pdf/2509.13386","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2509.13386","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Lim, Hansol","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lim, Hansol","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120651350","display_name":"Minhyeok Im","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Im, Minhyeok","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002728520","display_name":"Jonathan Boyack","orcid":"https://orcid.org/0000-0003-1790-5335"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Boyack, Jonathan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017878620","display_name":"Jee Won Lee","orcid":"https://orcid.org/0000-0002-3253-749X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Jee Won","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5111313355","display_name":"Jongseong Brad Choi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Choi, Jongseong Brad","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10808","display_name":"Electric and Hybrid Vehicle Technologies","score":0.2874999940395355,"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/T10808","display_name":"Electric and Hybrid Vehicle Technologies","score":0.2874999940395355,"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/T10768","display_name":"Electric Vehicles and Infrastructure","score":0.24660000205039978,"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/T10524","display_name":"Traffic control and management","score":0.15770000219345093,"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/electric-vehicle","display_name":"Electric vehicle","score":0.6061000227928162},{"id":"https://openalex.org/keywords/dwell-time","display_name":"Dwell time","score":0.5898000001907349},{"id":"https://openalex.org/keywords/operator","display_name":"Operator (biology)","score":0.49410000443458557},{"id":"https://openalex.org/keywords/vehicle-dynamics","display_name":"Vehicle dynamics","score":0.46459999680519104},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.46000000834465027},{"id":"https://openalex.org/keywords/track","display_name":"Track (disk drive)","score":0.4438999891281128},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.42649999260902405},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.396699994802475},{"id":"https://openalex.org/keywords/powertrain","display_name":"Powertrain","score":0.3921000063419342}],"concepts":[{"id":"https://openalex.org/C2776422217","wikidata":"https://www.wikidata.org/wiki/Q13629441","display_name":"Electric vehicle","level":3,"score":0.6061000227928162},{"id":"https://openalex.org/C151637689","wikidata":"https://www.wikidata.org/wiki/Q5318064","display_name":"Dwell time","level":2,"score":0.5898000001907349},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5067999958992004},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.49410000443458557},{"id":"https://openalex.org/C79487989","wikidata":"https://www.wikidata.org/wiki/Q934680","display_name":"Vehicle dynamics","level":2,"score":0.46459999680519104},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.46000000834465027},{"id":"https://openalex.org/C89992363","wikidata":"https://www.wikidata.org/wiki/Q5961558","display_name":"Track (disk drive)","level":2,"score":0.4438999891281128},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.42649999260902405},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.396699994802475},{"id":"https://openalex.org/C76047896","wikidata":"https://www.wikidata.org/wiki/Q1786258","display_name":"Powertrain","level":3,"score":0.3921000063419342},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.38830000162124634},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.37770000100135803},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.35370001196861267},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.35359999537467957},{"id":"https://openalex.org/C171146098","wikidata":"https://www.wikidata.org/wiki/Q124192","display_name":"Automotive engineering","level":1,"score":0.351500004529953},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3474999964237213},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.3467000126838684},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.34310001134872437},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.3343000113964081},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.33320000767707825},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.32420000433921814},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31690001487731934},{"id":"https://openalex.org/C13393347","wikidata":"https://www.wikidata.org/wiki/Q8424","display_name":"Aerodynamics","level":2,"score":0.3061999976634979},{"id":"https://openalex.org/C2778538500","wikidata":"https://www.wikidata.org/wiki/Q3427","display_name":"Vega","level":2,"score":0.30300000309944153},{"id":"https://openalex.org/C19966478","wikidata":"https://www.wikidata.org/wiki/Q4810574","display_name":"Mobile robot","level":3,"score":0.2937999963760376},{"id":"https://openalex.org/C2776999362","wikidata":"https://www.wikidata.org/wiki/Q2349274","display_name":"Planner","level":2,"score":0.29260000586509705},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.2838999927043915},{"id":"https://openalex.org/C145424490","wikidata":"https://www.wikidata.org/wiki/Q618465","display_name":"Remotely operated underwater vehicle","level":4,"score":0.25529998540878296}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2509.13386","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.13386","pdf_url":"https://arxiv.org/pdf/2509.13386","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2509.13386","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2509.13386","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2509.13386","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.13386","pdf_url":"https://arxiv.org/pdf/2509.13386","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0],"present":[1],"VEGA,":[2],"a":[3,29,58,66,106,115],"vehicle-adaptive":[4],"energy-aware":[5],"routing":[6],"system":[7],"for":[8],"electric":[9],"vehicles":[10],"(EVs)":[11],"that":[12,34,64],"integrates":[13],"physics-informed":[14,30],"parameter":[15],"estimation":[16],"with":[17,92,101,119],"RL-based":[18],"charge-aware":[19],"path":[20],"planning.":[21],"VEGA":[22,113],"consists":[23],"of":[24,51],"two":[25],"copupled":[26],"modules:":[27],"(1)":[28],"neural":[31],"operator":[32],"(PINO)":[33],"estimates":[35],"vehicle-specific":[36],"physical":[37],"parameters-drag,":[38],"rolling":[39],"resistance,":[40],"mass,":[41],"motor":[42],"and":[43,46,54,73,90,124,136,142,160],"regenerative-braking":[44],"efficiencies,":[45],"auxiliary":[47],"load-from":[48],"short":[49],"windows":[50],"onboard":[52],"speed":[53],"acceleration":[55],"data;":[56],"(2)":[57],"Proximal":[59],"Policy":[60],"Optimization":[61],"(PPO)":[62],"agent":[63,80],"navigates":[65],"charger-annotated":[67],"road":[68,156],"graph,":[69],"jointly":[70],"selecting":[71],"routes":[72],"charging":[74],"stops":[75],"under":[76],"state-of-charge":[77],"constraints.":[78],"The":[79,149],"is":[81,146],"initialized":[82],"via":[83],"behavior":[84],"cloning":[85],"from":[86],"an":[87],"A*":[88,132],"teacher":[89],"fine-tuned":[91],"cirriculum-guided":[93],"PPO":[94],"on":[95],"the":[96,129,134],"full":[97],"U.S.":[98],"highway":[99],"network":[100],"Tesla":[102],"Supercharger":[103],"locations.":[104],"On":[105],"cross-country":[107],"San":[108],"Francisco-to-New":[109],"York":[110],"route":[111],"(~4,860km),":[112],"produces":[114],"feasible":[116],"20-stop":[117],"plan":[118],"56.12h":[120],"total":[121],"trip":[122],"time":[123],"minimum":[125],"SoC":[126],"11.41%.":[127],"Against":[128],"controlled":[130],"Energy-aware":[131],"baseline,":[133],"distance":[135],"driving-time":[137],"gaps":[138],"are":[139],"small":[140],"(-8.49km":[141],"+0.37h),":[143],"while":[144],"inference":[145],"&gt;20x":[147],"faster.":[148],"learned":[150],"policy":[151],"generalizes":[152],"without":[153],"retraining":[154],"to":[155],"networks":[157],"in":[158],"France":[159],"Japan.":[161]},"counts_by_year":[],"updated_date":"2026-08-16T07:02:28.622633","created_date":"2025-10-10T00:00:00"}
