{"id":"https://openalex.org/W6948158743","doi":"https://doi.org/10.48550/arxiv.2508.02858","title":"Empowering Microscopic Traffic Simulators with Realistic Perception using Surrogate Sensor Models","display_name":"Empowering Microscopic Traffic Simulators with Realistic Perception using Surrogate Sensor Models","publication_year":2025,"publication_date":"2025-08-04","ids":{"openalex":"https://openalex.org/W6948158743","doi":"https://doi.org/10.48550/arxiv.2508.02858"},"language":"en","primary_location":{"id":"doi:10.48550/arxiv.2508.02858","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2508.02858","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2508.02858","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Zhu, Tianheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Zhu, Tianheng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Feng, Yiheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng, Yiheng","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"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":true,"primary_topic":{"id":"https://openalex.org/T13568","display_name":"Wood and Agarwood Research","score":0.1054999977350235,"subfield":{"id":"https://openalex.org/subfields/1605","display_name":"Organic Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13568","display_name":"Wood and Agarwood Research","score":0.1054999977350235,"subfield":{"id":"https://openalex.org/subfields/1605","display_name":"Organic Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13130","display_name":"Biological and pharmacological studies of plants","score":0.06759999692440033,"subfield":{"id":"https://openalex.org/subfields/2736","display_name":"Pharmacology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10895","display_name":"Species Distribution and Climate Change","score":0.0421999990940094,"subfield":{"id":"https://openalex.org/subfields/2302","display_name":"Ecological Modeling"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.8460000157356262},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.47279998660087585},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.41850000619888306},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.39169999957084656},{"id":"https://openalex.org/keywords/false-positive-paradox","display_name":"False positive paradox","score":0.3840999901294708},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.38019999861717224},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.33079999685287476},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.31279999017715454}],"concepts":[{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.8460000157356262},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.791700005531311},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5102999806404114},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.47279998660087585},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4661000072956085},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.41850000619888306},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.39169999957084656},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.3840999901294708},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.38019999861717224},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.33079999685287476},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.31279999017715454},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3012999892234802},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2994000017642975},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.29030001163482666},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.28060001134872437},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.2800000011920929},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.27000001072883606},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.2678999900817871},{"id":"https://openalex.org/C29081049","wikidata":"https://www.wikidata.org/wiki/Q1364242","display_name":"Image stitching","level":2,"score":0.26660001277923584},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.266400009393692},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2590999901294708},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.257099986076355},{"id":"https://openalex.org/C2776821279","wikidata":"https://www.wikidata.org/wiki/Q2293902","display_name":"Mobile mapping","level":3,"score":0.2556999921798706},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2508.02858","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2508.02858","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":"doi:10.48550/arxiv.2508.02858","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2508.02858","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":"article"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.4482351541519165}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Simulation":[0],"is":[1],"central":[2],"to":[3,68,197],"the":[4,121,179],"evaluation":[5],"of":[6,28,65,126,158,173],"intelligent":[7],"transportation":[8],"system":[9],"(ITS)":[10],"applications.":[11],"As":[12],"ITS":[13,183],"increasingly":[14],"incorporates":[15],"autonomous":[16],"vehicle":[17,190],"(AV)":[18],"technologies":[19],"as":[20,213,215],"fleet":[21],"vehicles":[22],"and/or":[23],"mobile":[24],"sensors,":[25],"accurate":[26],"modeling":[27,84],"their":[29],"perception":[30,42,83,221],"capabilities":[31],"becomes":[32],"essential":[33],"in":[34,57,160],"high-fidelity":[35],"simulations.":[36,235],"While":[37],"game-engine-based":[38],"simulators":[39,76],"reproduce":[40],"realistic":[41,100,210],"environments":[43],"through":[44],"3D":[45],"scene":[46],"rendering":[47],"and":[48,115,124,133,141,170,189,202,238],"raw":[49],"sensor":[50],"data":[51,177,239],"generation,":[52],"they":[53],"face":[54],"scalability":[55],"challenges":[56],"simulating":[58],"traffic":[59,75,234],"networks":[60],"with":[61,175,195],"a":[62,93,138,142,150],"large":[63],"number":[64],"AVs":[66],"due":[67],"high":[69],"computational":[70,226],"cost.":[71],"In":[72],"contrast,":[73],"microscopic":[74],"(MTS)":[77],"can":[78],"scale":[79],"efficiently":[80],"but":[81],"lack":[82],"capabilities.":[85],"To":[86,129],"bridge":[87],"this":[88],"gap,":[89],"we":[90],"propose":[91],"MIDAR,":[92],"surrogate":[94],"LiDAR":[95,101,117,131,162],"detection":[96,163,211],"model":[97],"that":[98,206],"mimics":[99],"detections":[102,118],"using":[103,165],"only":[104],"high-level":[105],"features":[106],"readily":[107],"available":[108,242],"from":[109,178],"MTS.":[110],"Specifically,":[111],"MIDAR":[112,136,154,196,207],"predicts":[113],"true-positive":[114],"false-negative":[116],"based":[119],"on":[120],"relative":[122],"positions":[123],"dimensions":[125],"surrounding":[127],"objects.":[128],"capture":[130],"visibility":[132],"occlusion":[134],"effects,":[135],"introduces":[137],"ray-hit":[139],"feature":[140],"Refined":[143],"Multi-hop":[144],"Line-of-Sight":[145],"(RM-LoS)":[146],"graph":[147],"processed":[148],"by":[149],"geometry-aware":[151],"Graph":[152],"Transformer.":[153],"achieves":[155],"an":[156,171],"AUC":[157,172],"0.94":[159],"approximating":[161],"results":[164],"CARLA-generated":[166],"point":[167],"cloud":[168],"data,":[169],"0.86":[174],"real-world":[176],"nuScenes":[180],"dataset.":[181],"Two":[182],"applications,":[184],"cooperative-perception-based":[185],"adaptive":[186],"signal":[187],"control":[188],"trajectory":[191],"reconstruction,":[192],"are":[193,240],"integrated":[194],"further":[198],"validate":[199],"its":[200],"realism":[201],"necessity.":[203],"Results":[204],"show":[205],"generates":[208],"more":[209],"outputs":[212],"well":[214],"application-level":[216],"performance":[217],"metrics":[218],"than":[219],"simplified":[220],"models":[222],"while":[223],"introducing":[224],"minimal":[225],"overhead,":[227],"enabling":[228],"seamless":[229],"integration":[230],"into":[231],"large-scale,":[232],"real-time":[233],"The":[236],"code":[237],"publicly":[241],"at":[243],"https://github.com/Purdue-CART-Lab/MIDAR.":[244]},"counts_by_year":[],"updated_date":"2026-03-11T06:11:40.159057","created_date":"2025-10-10T00:00:00"}
