{"id":"https://openalex.org/W7130698451","doi":"https://doi.org/10.1109/tits.2025.3641393","title":"Generation of High-Coverage Traffic Scenarios for Efficient Simulation Testing of Automated Driving Systems","display_name":"Generation of High-Coverage Traffic Scenarios for Efficient Simulation Testing of Automated Driving Systems","publication_year":2026,"publication_date":"2026-02-20","ids":{"openalex":"https://openalex.org/W7130698451","doi":"https://doi.org/10.1109/tits.2025.3641393"},"language":null,"primary_location":{"id":"doi:10.1109/tits.2025.3641393","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2025.3641393","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126460530","display_name":"Penghui Li","orcid":null},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Penghui Li","raw_affiliation_strings":["School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-4709-6874","affiliations":[{"raw_affiliation_string":"School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104133096","display_name":"Henan Yuan","orcid":null},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Henan Yuan","raw_affiliation_strings":["School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126452875","display_name":"Qianru Dong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qianru Dong","raw_affiliation_strings":["Beijing Automotive Technology Center Co., Ltd., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Automotive Technology Center Co., Ltd., Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010021584","display_name":"Ruidong Yan","orcid":"https://orcid.org/0000-0002-9479-6489"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruidong Yan","raw_affiliation_strings":["School of Systems Science, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Systems Science, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120870316","display_name":"Chunjiao Dong","orcid":null},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunjiao Dong","raw_affiliation_strings":["School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-4314-363X","affiliations":[{"raw_affiliation_string":"School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Chen Chen","orcid":"https://orcid.org/0000-0003-4310-8428"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chen Chen","raw_affiliation_strings":["College of Metropolitan Transportation, Beijing University of Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-4310-8428","affiliations":[{"raw_affiliation_string":"College of Metropolitan Transportation, Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076129978","display_name":"S. X. Li","orcid":"https://orcid.org/0000-0003-4923-3633"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shengbo Eben Li","raw_affiliation_strings":["School of Vehicle and Mobility and the College of AI, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-4923-3633","affiliations":[{"raw_affiliation_string":"School of Vehicle and Mobility and the College of AI, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5126460530"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":8.0338,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.95521521,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"27","issue":"3","first_page":"3208","last_page":"3222"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.39719998836517334,"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":0.39719998836517334,"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/T10524","display_name":"Traffic control and management","score":0.257999986410141,"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"}},{"id":"https://openalex.org/T10805","display_name":"Vehicle Dynamics and Control Systems","score":0.055399999022483826,"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/monte-carlo-method","display_name":"Monte Carlo method","score":0.5425999760627747},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.5385000109672546},{"id":"https://openalex.org/keywords/probability-density-function","display_name":"Probability density function","score":0.4984000027179718},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.46230000257492065},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.448199987411499},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.44679999351501465},{"id":"https://openalex.org/keywords/genetic-algorithm","display_name":"Genetic algorithm","score":0.4165000021457672},{"id":"https://openalex.org/keywords/probability-distribution","display_name":"Probability distribution","score":0.4065999984741211},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.3822000026702881}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5533999800682068},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.5425999760627747},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.5385000109672546},{"id":"https://openalex.org/C197055811","wikidata":"https://www.wikidata.org/wiki/Q207522","display_name":"Probability density function","level":2,"score":0.4984000027179718},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.46230000257492065},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.448199987411499},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.44679999351501465},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.4165000021457672},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.4065999984741211},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.40470001101493835},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.3822000026702881},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.35190001130104065},{"id":"https://openalex.org/C75782508","wikidata":"https://www.wikidata.org/wiki/Q3333633","display_name":"Cross-entropy method","level":4,"score":0.3513000011444092},{"id":"https://openalex.org/C176066374","wikidata":"https://www.wikidata.org/wiki/Q629118","display_name":"Fitness function","level":3,"score":0.35100001096725464},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3386000096797943},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.3359000086784363},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.329800009727478},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.32249999046325684},{"id":"https://openalex.org/C13153151","wikidata":"https://www.wikidata.org/wiki/Q1639846","display_name":"Hybrid Monte Carlo","level":4,"score":0.31929999589920044},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.290800005197525},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.28450000286102295},{"id":"https://openalex.org/C128942645","wikidata":"https://www.wikidata.org/wiki/Q1568346","display_name":"Test case","level":3,"score":0.2768999934196472},{"id":"https://openalex.org/C160947583","wikidata":"https://www.wikidata.org/wiki/Q2083147","display_name":"Distribution fitting","level":3,"score":0.272599995136261},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.2687999904155731},{"id":"https://openalex.org/C79487989","wikidata":"https://www.wikidata.org/wiki/Q934680","display_name":"Vehicle dynamics","level":2,"score":0.2687000036239624},{"id":"https://openalex.org/C170593435","wikidata":"https://www.wikidata.org/wiki/Q4128565","display_name":"Slice sampling","level":4,"score":0.267300009727478},{"id":"https://openalex.org/C80519477","wikidata":"https://www.wikidata.org/wiki/Q3532236","display_name":"Scenario testing","level":3,"score":0.26570001244544983},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.26019999384880066},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.259799987077713},{"id":"https://openalex.org/C56672385","wikidata":"https://www.wikidata.org/wiki/Q17157111","display_name":"Mixture distribution","level":3,"score":0.25540000200271606}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2025.3641393","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2025.3641393","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.804588258266449}],"awards":[{"id":"https://openalex.org/G3883902231","display_name":null,"funder_award_id":"20250484759","funder_id":"https://openalex.org/F4320334978","funder_display_name":"Beijing Nova Program"},{"id":"https://openalex.org/G6113830013","display_name":null,"funder_award_id":"52372422","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320334978","display_name":"Beijing Nova Program","ror":"https://ror.org/034k14f91"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2020276108","https://openalex.org/W2135194391","https://openalex.org/W2168175751","https://openalex.org/W2438413413","https://openalex.org/W2496483613","https://openalex.org/W2511072509","https://openalex.org/W2513534178","https://openalex.org/W2525936901","https://openalex.org/W2679723396","https://openalex.org/W2954040150","https://openalex.org/W2963298289","https://openalex.org/W2963657083","https://openalex.org/W2972588190","https://openalex.org/W3004750882","https://openalex.org/W3127647470","https://openalex.org/W4360849015","https://openalex.org/W4361193488","https://openalex.org/W4362650413","https://openalex.org/W4391019636"],"related_works":[],"abstract_inverted_index":{"Virtual":[0],"simulation":[1],"testing":[2],"is":[3,19,65,124,148,161,199],"crucial":[4],"for":[5,221],"ensuring":[6],"automated":[7],"vehicles":[8],"safety,":[9],"which":[10],"offers":[11],"low":[12],"cost":[13],"and":[14,37,93,107,121,131,167],"good":[15],"repeatability.":[16],"The":[17,145],"key":[18],"to":[20,30,68,89,111,126,140,163,178],"test":[21,38],"in":[22,137],"various":[23],"virtual":[24],"driving":[25,174],"scenarios,":[26],"but":[27],"often":[28],"fails":[29],"strike":[31],"a":[32,46,52,62,80,84,94,172,195,217],"balance":[33],"between":[34,118],"scenario":[35,47,76,129,166,181,191],"coverage":[36,218,240],"efficiency.":[39],"To":[40],"address":[41],"this":[42],"issue,":[43],"we":[44],"propose":[45],"generation":[48],"method":[49,147,235],"based":[50],"on":[51],"Genetic":[53,138],"Algorithm":[54,139],"optimized":[55],"Hamiltonian":[56,81],"Monte":[57,230],"Carlo":[58,231],"sampling":[59,99,232],"approach.":[60],"Specifically,":[61],"Markov":[63,228],"chain":[64],"constructed":[66],"converging":[67],"the":[69,90,98,112,128,134,142,165,180,184,206,222,226],"joint":[70,185],"probability":[71,105,186],"density":[72,188],"distribution":[73,92,187],"function":[74,82,136,189],"of":[75,190,219],"parameters.":[77,144,182],"By":[78],"defining":[79],"with":[83,216,225,241],"potential":[85],"energy":[86,96],"term":[87],"related":[88],"posterior":[91],"kinetic":[95],"term,":[97],"process":[100],"moves":[101],"efficiently":[102],"towards":[103],"high":[104],"regions":[106],"achieves":[108],"faster":[109],"convergence":[110],"target":[113],"distribution.":[114],"Moreover,":[115],"Jensen-Shannon":[116],"divergence":[117],"generated":[119,215],"samples":[120],"raw":[122],"data":[123],"proposed":[125,146,162,203],"evaluate":[127],"coverage,":[130],"used":[132,177],"as":[133],"objective":[135],"optimize":[141],"algorithm":[143],"validated":[149],"by":[150,194],"lead":[151,210],"vehicle":[152,211],"deceleration":[153,159,212],"scenarios":[154,213],"generation,":[155],"where":[156],"an":[157],"S-shaped":[158],"model":[160,198],"parameterize":[164],"22,343":[168],"segments":[169],"extracted":[170],"from":[171],"naturalistic":[173],"dataset":[175],"are":[176,214],"calibrate":[179],"Subsequently,":[183],"parameters":[192],"fitted":[193],"gaussian":[196],"mixture":[197],"imported":[200],"into":[201],"our":[202,234],"method.":[204],"As":[205],"results,":[207],"1,649":[208],"concrete":[209],"99.14%":[220],"dataset.":[223],"Compared":[224],"previous":[227],"Chain":[229],"method,":[233],"achieved":[236],"9":[237],"times":[238,243],"higher":[239],"28":[242],"fewer":[244],"scenarios.":[245]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-06-03T09:05:47.796612","created_date":"2026-02-21T00:00:00"}
