{"id":"https://openalex.org/W7128677315","doi":"https://doi.org/10.48550/arxiv.2602.10458","title":"Found-RL: foundation model-enhanced reinforcement learning for autonomous driving","display_name":"Found-RL: foundation model-enhanced reinforcement learning for autonomous driving","publication_year":2026,"publication_date":"2026-02-11","ids":{"openalex":"https://openalex.org/W7128677315","doi":"https://doi.org/10.48550/arxiv.2602.10458"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.10458","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5125721766","display_name":"Yansong Qu","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Qu, Yansong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125732602","display_name":"Zihao Sheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sheng, Zihao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125750295","display_name":"Zilin Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Zilin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121559629","display_name":"Jiancong Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Jiancong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102234585","display_name":"Yan Luo","orcid":"https://orcid.org/0009-0004-9424-9145"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo, Yuhao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125760213","display_name":"Tianyi Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Tianyi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011085340","display_name":"Yiheng Feng","orcid":"https://orcid.org/0000-0001-5656-3222"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng, Yiheng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125687730","display_name":"Samuel Labi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Labi, Samuel","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5121259100","display_name":"Sikai Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Sikai","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5125721766"],"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.3481000065803528,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.3481000065803528,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.22370000183582306,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.12110000103712082,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.8183000087738037},{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.7103000283241272},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.580299973487854},{"id":"https://openalex.org/keywords/regret","display_name":"Regret","score":0.43860000371932983},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.4146000146865845},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.3783000111579895},{"id":"https://openalex.org/keywords/asynchronous-communication","display_name":"Asynchronous communication","score":0.3596000075340271},{"id":"https://openalex.org/keywords/bridging","display_name":"Bridging (networking)","score":0.32330000400543213}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8183000087738037},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.7103000283241272},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6919000148773193},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5932999849319458},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.580299973487854},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46219998598098755},{"id":"https://openalex.org/C50817715","wikidata":"https://www.wikidata.org/wiki/Q79895177","display_name":"Regret","level":2,"score":0.43860000371932983},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.4146000146865845},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.3783000111579895},{"id":"https://openalex.org/C151319957","wikidata":"https://www.wikidata.org/wiki/Q752739","display_name":"Asynchronous communication","level":2,"score":0.3596000075340271},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.32330000400543213},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.322299987077713},{"id":"https://openalex.org/C2778869765","wikidata":"https://www.wikidata.org/wiki/Q6028363","display_name":"Inefficiency","level":2,"score":0.3001999855041504},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.27889999747276306},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.2752000093460083},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.27059999108314514},{"id":"https://openalex.org/C149810388","wikidata":"https://www.wikidata.org/wiki/Q5374873","display_name":"Emulation","level":2,"score":0.2694000005722046},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.26089999079704285},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.2581000030040741},{"id":"https://openalex.org/C166109690","wikidata":"https://www.wikidata.org/wiki/Q4677422","display_name":"Action selection","level":3,"score":0.2533999979496002},{"id":"https://openalex.org/C46743427","wikidata":"https://www.wikidata.org/wiki/Q1341685","display_name":"Inference engine","level":3,"score":0.25040000677108765}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.10458","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.10458","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.10458","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.10458","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"score":0.6093177795410156,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Reinforcement":[0],"Learning":[1],"(RL)":[2],"has":[3],"emerged":[4],"as":[5],"a":[6,21,62,151,171],"dominant":[7],"paradigm":[8],"for":[9,69,129,164],"end-to-end":[10,162],"autonomous":[11],"driving":[12],"(AD).":[13],"However,":[14],"RL":[15,52,68,122,173],"suffers":[16],"from":[17,88,155],"sample":[18],"inefficiency":[19],"and":[20,108,149,168,192],"lack":[22],"of":[23],"semantic":[24],"interpretability":[25],"in":[26,50],"complex":[27],"scenarios.":[28],"Foundation":[29],"Models,":[30],"particularly":[31],"Vision-Language":[32],"Models":[33],"(VLMs),":[34],"can":[35,175],"mitigate":[36],"this":[37,57],"by":[38],"offering":[39],"rich,":[40],"context-aware":[41],"knowledge,":[42],"yet":[43],"their":[44],"high":[45],"inference":[46,81,186],"latency":[47,94],"hinders":[48],"deployment":[49],"high-frequency":[51],"training":[53],"loops.":[54],"To":[55],"bridge":[56],"gap,":[58],"we":[59,125],"present":[60],"Found-RL,":[61],"platform":[63],"tailored":[64],"to":[65,96,113],"efficiently":[66],"enhance":[67],"AD":[70],"using":[71],"foundation":[72],"models.":[73],"A":[74],"core":[75],"innovation":[76],"is":[77],"the":[78,89,121],"asynchronous":[79],"batch":[80],"framework,":[82],"which":[83,143],"decouples":[84],"heavy":[85],"VLM":[86,117,166],"reasoning":[87],"simulation":[90],"loop,":[91],"effectively":[92,114],"resolving":[93],"bottlenecks":[95],"support":[97],"real-time":[98,185],"learning.":[99],"We":[100,133],"introduce":[101],"diverse":[102],"supervision":[103],"mechanisms:":[104],"Value-Margin":[105],"Regularization":[106],"(VMR)":[107],"Advantage-Weighted":[109],"Action":[110,141],"Guidance":[111],"(AWAG)":[112],"distill":[115],"expert-like":[116],"action":[118],"suggestions":[119],"into":[120],"policy.":[123],"Additionally,":[124],"adopt":[126],"high-throughput":[127],"CLIP":[128],"dense":[130],"reward":[131],"shaping.":[132],"address":[134],"CLIP's":[135],"dynamic":[136],"blindness":[137],"via":[138],"Conditional":[139],"Contrastive":[140],"Alignment,":[142],"conditions":[144],"prompts":[145],"on":[146],"discretized":[147],"speed/command":[148],"yields":[150],"normalized,":[152],"margin-based":[153],"bonus":[154],"context-specific":[156],"action-anchor":[157],"scoring.":[158],"Found-RL":[159],"provides":[160],"an":[161],"pipeline":[163],"fine-tuned":[165],"integration":[167],"shows":[169],"that":[170],"lightweight":[172],"model":[174],"achieve":[176],"near-VLM":[177],"performance":[178],"compared":[179],"with":[180],"billion-parameter":[181],"VLMs":[182],"while":[183],"sustaining":[184],"(approx.":[187],"500":[188],"FPS).":[189],"Code,":[190],"data,":[191],"models":[193],"will":[194],"be":[195],"publicly":[196],"available":[197],"at":[198],"https://github.com/ys-qu/found-rl.":[199]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-13T00:00:00"}
