{"id":"https://openalex.org/W7162464503","doi":"https://doi.org/10.48550/arxiv.2605.25393","title":"Decision-Making with Lightweight Confidence-Aware Language Model for Autonomous Driving","display_name":"Decision-Making with Lightweight Confidence-Aware Language Model for Autonomous Driving","publication_year":2026,"publication_date":"2026-05-25","ids":{"openalex":"https://openalex.org/W7162464503","doi":"https://doi.org/10.48550/arxiv.2605.25393"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.25393","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.25393","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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.25393","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137060545","display_name":"Ruoyu Yao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yao, Ruoyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102616478","display_name":"Ruiguo Zhong","orcid":"https://orcid.org/0009-0009-3711-892X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhong, Ruiguo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137012708","display_name":"Pei Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Pei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137038715","display_name":"Mingxing Peng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peng, Mingxing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137074453","display_name":"Rui Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Rui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137083463","display_name":"Jun Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Jun","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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.2944999933242798,"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"}},"topics":[{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.2944999933242798,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.1492999941110611,"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":0.08569999784231186,"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/inference","display_name":"Inference","score":0.6877999901771545},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.6288999915122986},{"id":"https://openalex.org/keywords/automatic-summarization","display_name":"Automatic summarization","score":0.5382000207901001},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.44690001010894775},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.39809998869895935},{"id":"https://openalex.org/keywords/modeling-language","display_name":"Modeling language","score":0.3424000144004822},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.3418000042438507},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.3393000066280365}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7554000020027161},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6877999901771545},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.6288999915122986},{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.5382000207901001},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5375999808311462},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.44690001010894775},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4399000108242035},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.39809998869895935},{"id":"https://openalex.org/C179603123","wikidata":"https://www.wikidata.org/wiki/Q1941921","display_name":"Modeling language","level":3,"score":0.3424000144004822},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.3418000042438507},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.3393000066280365},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.3377000093460083},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.3328000009059906},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.33059999346733093},{"id":"https://openalex.org/C2983448237","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Language understanding","level":2,"score":0.3294999897480011},{"id":"https://openalex.org/C59594135","wikidata":"https://www.wikidata.org/wiki/Q5249242","display_name":"Decision model","level":2,"score":0.3070000112056732},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.30329999327659607},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.29980000853538513},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.26840001344680786},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.262800008058548},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.2583000063896179}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.25393","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.25393","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":"doi:10.48550/arxiv.2605.25393","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.25393","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.7387846112251282,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"Language":[1],"Models":[2],"(LLMs)":[3],"and":[4,20,28,69,84,119,145,167],"Multimodal":[5],"LLMs":[6],"(MLLMs)":[7],"have":[8],"demonstrated":[9],"immense":[10],"potential":[11],"in":[12,40,164],"autonomous":[13],"driving":[14],"(AD)":[15],"by":[16],"offering":[17],"human-like":[18],"reasoning":[19,68],"open-world":[21],"generalization.":[22],"However,":[23],"the":[24,62,113,120,142,152],"excessive":[25],"computational":[26],"overhead":[27],"high":[29],"inference":[30,173],"latency":[31],"of":[32,116,122],"these":[33],"massive":[34],"models":[35],"severely":[36],"hinder":[37],"their":[38],"deployment":[39],"resource-constrained":[41],"AD":[42],"systems.":[43],"To":[44],"address":[45],"this":[46],"challenge,":[47],"we":[48,73],"propose":[49],"a":[50,55,75,104,109,130],"novel":[51],"decision-making":[52],"framework":[53],"utilizing":[54],"lightweight":[56,105],"confidence-aware":[57,131],"language":[58,106],"model,":[59],"which":[60],"bridges":[61],"gap":[63],"between":[64],"complex":[65],"multimodal":[66],"intention":[67],"efficient":[70],"inference.":[71],"Specifically,":[72],"design":[74],"multi-agent":[76],"collaborative":[77],"workflow,":[78],"comprising":[79],"action":[80],"voting,":[81],"confidence":[82],"assessment,":[83],"summarization":[85],"agents,":[86],"to":[87,140],"generate":[88],"high-quality,":[89],"confidence-annotated":[90],"decision":[91,117],"demonstrations":[92,99],"via":[93,129],"explicit":[94],"Chain-of-Thought":[95],"(CoT)":[96],"reasoning.":[97],"These":[98],"are":[100],"then":[101],"distilled":[102],"into":[103],"model":[107],"featuring":[108],"dual-head":[110],"architecture,":[111],"enabling":[112],"joint":[114],"prediction":[115],"probabilities":[118],"generation":[121],"textual":[123],"rationales.":[124],"The":[125],"distillation":[126],"is":[127],"realized":[128],"fine-tuning":[132],"strategy":[133],"coupled":[134],"with":[135],"Retrieval":[136],"Augmented":[137],"Generation":[138],"(RAG)":[139],"enhance":[141],"model's":[143],"adaptability":[144],"data":[146],"efficiency.":[147],"Comprehensive":[148],"closed-loop":[149],"experiments":[150],"on":[151],"nuPlan":[153],"benchmark":[154],"demonstrate":[155],"that":[156],"our":[157],"approach":[158],"achieves":[159],"state-of-the-art":[160],"(SOTA)":[161],"success":[162],"rates":[163],"both":[165],"regular":[166],"long-tail":[168],"scenarios":[169],"while":[170],"maintaining":[171],"low":[172],"latency.":[174]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-27T00:00:00"}
