{"id":"https://openalex.org/W3205041048","doi":"https://doi.org/10.1109/tits.2021.3114816","title":"Deep Crash Detection From Vehicular Sensor Data With Multimodal Self-Supervision","display_name":"Deep Crash Detection From Vehicular Sensor Data With Multimodal Self-Supervision","publication_year":2021,"publication_date":"2021-10-10","ids":{"openalex":"https://openalex.org/W3205041048","doi":"https://doi.org/10.1109/tits.2021.3114816","mag":"3205041048"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2021.3114816","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2021.3114816","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/A5054861755","display_name":"Luca Kubin","orcid":"https://orcid.org/0000-0002-1017-646X"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Luca Kubin","raw_affiliation_strings":["Verizon Connect Research, Florence, Italy"],"affiliations":[{"raw_affiliation_string":"Verizon Connect Research, Florence, Italy","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030436209","display_name":"Tommaso Bianconcini","orcid":"https://orcid.org/0000-0003-3306-568X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tommaso Bianconcini","raw_affiliation_strings":["Verizon Connect Research, Florence, Italy"],"affiliations":[{"raw_affiliation_string":"Verizon Connect Research, Florence, Italy","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030990358","display_name":"Douglas Coimbra de Andrade","orcid":"https://orcid.org/0000-0001-8981-143X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Douglas Coimbra de Andrade","raw_affiliation_strings":["Verizon Connect Research, Florence, Italy"],"affiliations":[{"raw_affiliation_string":"Verizon Connect Research, Florence, Italy","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087236050","display_name":"Matteo Simoncini","orcid":"https://orcid.org/0000-0001-7128-1792"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Matteo Simoncini","raw_affiliation_strings":["Verizon Connect Research, Florence, Italy"],"affiliations":[{"raw_affiliation_string":"Verizon Connect Research, Florence, Italy","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058185763","display_name":"Leonardo Taccari","orcid":"https://orcid.org/0000-0003-0800-4893"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Leonardo Taccari","raw_affiliation_strings":["Verizon Connect Research, Florence, Italy"],"affiliations":[{"raw_affiliation_string":"Verizon Connect Research, Florence, Italy","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017560687","display_name":"Francesco Sambo","orcid":"https://orcid.org/0000-0002-7726-5811"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Francesco Sambo","raw_affiliation_strings":["Verizon Connect Research, Florence, Italy"],"affiliations":[{"raw_affiliation_string":"Verizon Connect Research, Florence, Italy","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5054861755"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9797,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.80931693,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"23","issue":"8","first_page":"12480","last_page":"12489"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.996399998664856,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.996399998664856,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9944999814033508,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.993399977684021,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/inertial-measurement-unit","display_name":"Inertial measurement unit","score":0.7198425531387329},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6693727970123291},{"id":"https://openalex.org/keywords/global-positioning-system","display_name":"Global Positioning System","score":0.6603591442108154},{"id":"https://openalex.org/keywords/gyroscope","display_name":"Gyroscope","score":0.6063117980957031},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.553650438785553},{"id":"https://openalex.org/keywords/accelerometer","display_name":"Accelerometer","score":0.5455649495124817},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5276196002960205},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49719956517219543},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.28161799907684326}],"concepts":[{"id":"https://openalex.org/C79061980","wikidata":"https://www.wikidata.org/wiki/Q941680","display_name":"Inertial measurement unit","level":2,"score":0.7198425531387329},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6693727970123291},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.6603591442108154},{"id":"https://openalex.org/C158488048","wikidata":"https://www.wikidata.org/wiki/Q483400","display_name":"Gyroscope","level":2,"score":0.6063117980957031},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.553650438785553},{"id":"https://openalex.org/C89805583","wikidata":"https://www.wikidata.org/wiki/Q192940","display_name":"Accelerometer","level":2,"score":0.5455649495124817},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5276196002960205},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49719956517219543},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.28161799907684326},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2021.3114816","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2021.3114816","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/3","display_name":"Good health and well-being","score":0.5199999809265137}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W1900747440","https://openalex.org/W1973058638","https://openalex.org/W1984674851","https://openalex.org/W2019844689","https://openalex.org/W2050493487","https://openalex.org/W2118505738","https://openalex.org/W2128590881","https://openalex.org/W2131490903","https://openalex.org/W2157331557","https://openalex.org/W2194775991","https://openalex.org/W2270470215","https://openalex.org/W2338318698","https://openalex.org/W2515503816","https://openalex.org/W2786161686","https://openalex.org/W2842511635","https://openalex.org/W2892035503","https://openalex.org/W2896457183","https://openalex.org/W2903820641","https://openalex.org/W2952454702","https://openalex.org/W2972810968","https://openalex.org/W3035060554","https://openalex.org/W3035524453","https://openalex.org/W3046296398","https://openalex.org/W3083891030","https://openalex.org/W3099025572","https://openalex.org/W3109083691","https://openalex.org/W3110866669","https://openalex.org/W3171007011","https://openalex.org/W4248317380","https://openalex.org/W4287812705","https://openalex.org/W4297808394","https://openalex.org/W6684665197","https://openalex.org/W6686207219","https://openalex.org/W6755207826","https://openalex.org/W6759891849","https://openalex.org/W6774314701","https://openalex.org/W6776700526","https://openalex.org/W6779326418","https://openalex.org/W6781295694","https://openalex.org/W6786614245"],"related_works":["https://openalex.org/W1973973903","https://openalex.org/W2545638156","https://openalex.org/W3195533899","https://openalex.org/W4238176532","https://openalex.org/W1493687195","https://openalex.org/W2181490969","https://openalex.org/W2532618464","https://openalex.org/W2124707714","https://openalex.org/W2997884855","https://openalex.org/W1946547943"],"abstract_inverted_index":{"The":[0,109],"ability":[1],"to":[2,15,19,48,93,100],"detect":[3],"vehicle":[4,140],"accidents":[5],"from":[6,40,135],"on-board":[7],"sensor":[8,69],"data":[9,88],"is":[10],"of":[11,35,52,65,87,105,127,147,156,161,168],"the":[12,50,67,95,102,106,159,166,174],"utmost":[13],"importance":[14],"provide":[16],"prompt":[17],"assistance":[18],"prevent":[20],"injuries":[21],"and":[22,45,74,83,99,122,142,163,177,181],"fatalities.":[23],"In":[24],"this":[25],"article,":[26],"we":[27],"present":[28],"a":[29,61,77,120],"novel":[30],"deep":[31],"learning":[32],"method":[33,111,150],"capable":[34,64],"analyzing":[36],"time":[37,132],"series":[38,133],"recorded":[39],"Inertial":[41],"Measurement":[42],"Units":[43],"(IMU)":[44],"GPS":[46,75],"devices":[47],"recognize":[49],"presence":[51],"an":[53,84,152],"accident":[54],"along":[55],"with":[56,138],"its":[57],"severity.":[58],"We":[59],"propose":[60],"neural":[62],"architecture":[63],"exploiting":[66],"different":[68,139,145],"streams":[70],"(i.e.,":[71],"acceleration,":[72],"gyroscope,":[73],"speed),":[76],"multimodal":[78],"contrastive":[79],"self-supervised":[80],"training":[81],"procedure,":[82],"ad-hoc":[85],"stack":[86],"augmentation":[89],"techniques,":[90],"specifically":[91],"designed":[92],"counteract":[94],"extreme":[96],"class":[97],"imbalance":[98],"improve":[101],"generalization":[103],"capabilities":[104],"whole":[107],"pipeline.":[108],"proposed":[110],"has":[112,178],"been":[113],"validated":[114],"against":[115],"several":[116],"state-of-the-art":[117],"methods":[118],"on":[119,144,190],"large":[121],"highly":[123],"imbalanced":[124],"dataset,":[125],"composed":[126],"more":[128],"than":[129],"200":[130],"thousand":[131],"collected":[134],"US":[136],"vehicles,":[137],"sizes":[141],"traveling":[143],"types":[146],"road.":[148],"Our":[149],"achieves":[151],"average-precision":[153],"score":[154],"(AP)":[155],"0.9":[157],"in":[158,165],"detection":[160,167],"crashes":[162],"0.76":[164],"severe":[169],"crashes,":[170],"significantly":[171],"outperforming":[172],"all":[173],"other":[175],"approaches,":[176],"small":[179],"footprint":[180],"latency,":[182],"so":[183],"that":[184],"it":[185],"can":[186],"easily":[187],"be":[188],"deployed":[189],"embedded":[191],"devices.":[192]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":5},{"year":2022,"cited_by_count":2}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}