{"id":"https://openalex.org/W7162408740","doi":"https://doi.org/10.48550/arxiv.2605.24531","title":"NudgeVAD: Language-Nudged End-to-End Driving via FiLM Residuals","display_name":"NudgeVAD: Language-Nudged End-to-End Driving via FiLM Residuals","publication_year":2026,"publication_date":"2026-05-23","ids":{"openalex":"https://openalex.org/W7162408740","doi":"https://doi.org/10.48550/arxiv.2605.24531"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.24531","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24531","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.2605.24531","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102588227","display_name":"Chieh-Chi Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Chieh-Chi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125041668","display_name":"Yu-Hsiang Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Yu-Hsiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137023272","display_name":"Yi-Ting Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Yi-Ting","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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.3418000042438507,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.3418000042438507,"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/T10586","display_name":"Robotic Path Planning Algorithms","score":0.32499998807907104,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.04179999977350235,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/planner","display_name":"Planner","score":0.6467999815940857},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.6150000095367432},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.599399983882904},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.5404999852180481},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.4327000081539154},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.4147000014781952}],"concepts":[{"id":"https://openalex.org/C2776999362","wikidata":"https://www.wikidata.org/wiki/Q2349274","display_name":"Planner","level":2,"score":0.6467999815940857},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.6150000095367432},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6031000018119812},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.599399983882904},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.5404999852180481},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4562000036239624},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.4327000081539154},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4291999936103821},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.4147000014781952},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.3540000021457672},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.302700012922287},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.28630000352859497},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2590999901294708},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.257999986410141},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.257099986076355},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.25679999589920044},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.25040000677108765},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.24531","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24531","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.2605.24531","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24531","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Natural-language":[0],"instructions":[1],"promise":[2],"controllable":[3],"end-to-end":[4],"driving,":[5],"but":[6,77],"their":[7],"benefit":[8],"can":[9],"be":[10],"hidden":[11],"when":[12,134],"planners":[13],"already":[14],"receive":[15],"reliable":[16,70],"high-level":[17],"commands.":[18],"We":[19,62],"propose":[20],"NudgeVAD,":[21],"a":[22,30,34,41,66,86],"frozen-planner":[23],"residual":[24,43],"framework":[25],"that":[26,124],"uses":[27],"language":[28,72,97,125],"as":[29],"calibrated":[31],"nudge":[32],"to":[33,48,104],"VAD":[35,88],"trajectory.":[36],"With":[37,69,93],"identity-initialized":[38],"FiLM":[39],"and":[40,114],"zero-initialized":[42],"head,":[44],"NudgeVAD":[45,64,108],"is":[46,126,131,139],"equivalent":[47],"the":[49,74,135],"frozen":[50],"planner":[51,76],"at":[52],"initialization,":[53],"so":[54],"learned":[55],"deviations":[56],"arise":[57],"only":[58],"from":[59],"language-conditioned":[60],"residuals.":[61],"evaluate":[63],"along":[65],"command-reliability":[67],"axis.":[68],"commands,":[71,95],"improves":[73],"initial":[75],"becomes":[78,98],"nearly":[79],"redundant":[80],"once":[81],"compared":[82],"against":[83],"VAD-FT":[84,116],"(UNCOND),":[85],"compute-matched":[87],"model":[89],"fine-tuned":[90],"without":[91],"language.":[92],"random":[94],"however,":[96],"essential:":[99],"detaching":[100],"text":[101,110],"degrades":[102],"ADE6s":[103],"3.166":[105],"m,":[106],"while":[107],"with":[109],"recovers":[111],"2.806":[112],"m":[113],"outperforms":[115],"(UNCOND)":[117],"by":[118],"0.312":[119],"m.":[120],"These":[121],"results":[122],"show":[123],"not":[127],"universally":[128],"additive;":[129],"it":[130],"most":[132],"valuable":[133],"categorical":[136],"command":[137],"channel":[138],"unreliable.":[140]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-27T00:00:00"}
