{"id":"https://openalex.org/W7160923466","doi":"https://doi.org/10.48550/arxiv.2605.10034","title":"Beyond Self-Play and Scale: A Behavior Benchmark for Generalization in Autonomous Driving","display_name":"Beyond Self-Play and Scale: A Behavior Benchmark for Generalization in Autonomous Driving","publication_year":2026,"publication_date":"2026-05-11","ids":{"openalex":"https://openalex.org/W7160923466","doi":"https://doi.org/10.48550/arxiv.2605.10034"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.10034","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.10034","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":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.2605.10034","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114389183","display_name":"Aron Distelzweig","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Distelzweig, Aron","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036836299","display_name":"Faris Janjo\u0161","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Janjo\u0161, Faris","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042671881","display_name":"Andreas Look","orcid":"https://orcid.org/0000-0002-7423-4091"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Look, Andreas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5105966501","display_name":"Anna Rothenh\u00e4usler","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rothenh\u00e4usler, Anna","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133140351","display_name":"Daniel Jost","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jost, Daniel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135927336","display_name":"Oliver Scheel","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Scheel, Oliver","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135942597","display_name":"Raghu Rajan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rajan, Raghu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035248329","display_name":"Daphne Cornelisse","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cornelisse, Daphne","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015277482","display_name":"Eugene Vinitsky","orcid":"https://orcid.org/0000-0003-2372-4944"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vinitsky, Eugene","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135935717","display_name":"Joschka Boedecker","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Boedecker, Joschka","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":0.8065000176429749,"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":0.8065000176429749,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.05779999867081642,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.045099999755620956,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.7857999801635742},{"id":"https://openalex.org/keywords/suite","display_name":"Suite","score":0.7627000212669373},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7337999939918518},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.6933000087738037},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6010000109672546},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.5810999870300293},{"id":"https://openalex.org/keywords/planner","display_name":"Planner","score":0.5752000212669373},{"id":"https://openalex.org/keywords/crash","display_name":"Crash","score":0.5281999707221985},{"id":"https://openalex.org/keywords/collision-avoidance","display_name":"Collision avoidance","score":0.45890000462532043}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7857999801635742},{"id":"https://openalex.org/C79581498","wikidata":"https://www.wikidata.org/wiki/Q1367530","display_name":"Suite","level":2,"score":0.7627000212669373},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7337999939918518},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.6933000087738037},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6635000109672546},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6010000109672546},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.5810999870300293},{"id":"https://openalex.org/C2776999362","wikidata":"https://www.wikidata.org/wiki/Q2349274","display_name":"Planner","level":2,"score":0.5752000212669373},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5389999747276306},{"id":"https://openalex.org/C183469790","wikidata":"https://www.wikidata.org/wiki/Q333501","display_name":"Crash","level":2,"score":0.5281999707221985},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5076000094413757},{"id":"https://openalex.org/C2780864053","wikidata":"https://www.wikidata.org/wiki/Q5147495","display_name":"Collision avoidance","level":3,"score":0.45890000462532043},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.43970000743865967},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.42800000309944153},{"id":"https://openalex.org/C113843644","wikidata":"https://www.wikidata.org/wiki/Q901882","display_name":"Interface (matter)","level":4,"score":0.3991999924182892},{"id":"https://openalex.org/C149629883","wikidata":"https://www.wikidata.org/wiki/Q660926","display_name":"Fraction (chemistry)","level":2,"score":0.35569998621940613},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.34850001335144043},{"id":"https://openalex.org/C2778134712","wikidata":"https://www.wikidata.org/wiki/Q1047307","display_name":"Bundle","level":2,"score":0.34200000762939453},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.3409999907016754},{"id":"https://openalex.org/C114073186","wikidata":"https://www.wikidata.org/wiki/Q2631895","display_name":"Automated planning and scheduling","level":2,"score":0.29829999804496765},{"id":"https://openalex.org/C151552104","wikidata":"https://www.wikidata.org/wiki/Q7705809","display_name":"Test suite","level":4,"score":0.2946999967098236},{"id":"https://openalex.org/C121704057","wikidata":"https://www.wikidata.org/wiki/Q352070","display_name":"Collision","level":2,"score":0.2903999984264374},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.28540000319480896},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.2782999873161316},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.2775999903678894},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.27320000529289246},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.27250000834465027},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.2581999897956848},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.25459998846054077}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.10034","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.10034","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.10034","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.10034","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.6331347227096558,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recent":[0],"Autonomous":[1],"Driving":[2],"(AD)":[3],"works":[4],"such":[5,25],"as":[6,17],"GigaFlow":[7],"and":[8,61,237],"PufferDrive":[9,73,114],"have":[10],"unlocked":[11],"Reinforcement":[12],"Learning":[13],"(RL)":[14],"at":[15,86,116],"scale":[16,87],"a":[18,48,117,147,178,190,252,257,261],"training":[19,235],"strategy":[20],"for":[21,38,77,96],"driving":[22,39],"policies.":[23],"Yet":[24],"policies":[26,82,199,223],"remain":[27],"disconnected":[28],"from":[29,151],"established":[30,93],"benchmarks,":[31],"leaving":[32],"the":[33,78,113,120,152,183,212],"performance":[34,161],"of":[35,66,119,193],"large-scale":[36],"RL":[37,85],"on":[40,91,158,247],"standardized":[41,128],"evaluations":[42],"unknown.":[43],"We":[44,145,188],"present":[45],"BehaviorBench":[46],"--":[47],"comprehensive":[49],"test":[50],"suite":[51,192],"that":[52,105,126,133,255],"closes":[53],"this":[54,248],"gap":[55],"along":[56],"three":[57],"axes:":[58],"Evaluation,":[59,67],"Complexity,":[60,123],"Behavior":[62,170],"Diversity.":[63,171],"In":[64],"terms":[65],"we":[68,100,124,168,250],"provide":[69,189],"an":[70,92,102,220],"interface":[71],"connecting":[72],"to":[74,88,108,197,233,239,241],"nuPlan,":[75],"which,":[76],"first":[79],"time,":[80],"enables":[81],"trained":[83,224],"via":[84,225],"be":[89,109],"evaluated":[90],"planning":[94],"benchmark":[95],"autonomous":[97],"driving.":[98],"Complementarily,":[99],"offer":[101],"evaluation":[103],"framework":[104],"allows":[106],"planners":[107,176],"benchmarked":[110],"directly":[111],"inside":[112],"simulation,":[115],"fraction":[118],"time.":[121],"Regarding":[122],"observe":[125],"today's":[127],"benchmarks":[129,173],"are":[130,136],"so":[131],"simple":[132],"near-perfect":[134],"scores":[135],"achievable":[137],"by":[138],"straight":[139],"lane":[140],"following":[141,213],"with":[142,260],"collision":[143],"checking.":[144],"extract":[146],"meaningful,":[148],"interaction-rich":[149],"split":[150],"Waymo":[153],"Open":[154],"Motion":[155],"Dataset":[156],"(WOMD)":[157],"which":[159],"strong":[160],"is":[162],"impossible":[163],"without":[164],"multi-agent":[165],"reasoning.":[166],"Lastly,":[167],"address":[169],"Existing":[172],"commonly":[174],"evaluate":[175],"against":[177],"single":[179],"rule-based":[180,262],"traffic":[181,195,243],"model,":[182],"Intelligent":[184],"Driver":[185],"Model":[186],"(IDM).":[187],"diverse":[191],"interactive":[194,217],"agents":[196],"stress-test":[198],"under":[200,228],"heterogeneous":[201],"behaviors,":[202],"beyond":[203],"just":[204],"using":[205],"IDM.":[206],"Overall,":[207],"our":[208],"benchmarking":[209],"analysis":[210],"uncovers":[211],"insight:":[214],"despite":[215],"learning":[216],"behaviors":[218],"in":[219],"emergent":[221],"manner,":[222],"pure":[226],"self-play":[227],"standard":[229],"reward":[230],"functions":[231],"overfit":[232],"their":[234],"opponents":[236],"fail":[238],"generalize":[240],"other":[242],"agent":[244],"behaviors.":[245],"Building":[246],"observation,":[249],"propose":[251],"hybrid":[253],"planner":[254],"combines":[256],"PPO":[258],"policy":[259],"planner.":[263]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-13T00:00:00"}
