{"id":"https://openalex.org/W3011408759","doi":"https://doi.org/10.1109/sips47522.2019.9020572","title":"Efficiently Learning a Robust Self-Driving Model with Neuron Coverage Aware Adaptive Filter Reuse","display_name":"Efficiently Learning a Robust Self-Driving Model with Neuron Coverage Aware Adaptive Filter Reuse","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W3011408759","doi":"https://doi.org/10.1109/sips47522.2019.9020572","mag":"3011408759"},"language":"en","primary_location":{"id":"doi:10.1109/sips47522.2019.9020572","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sips47522.2019.9020572","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","raw_type":"proceedings-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/A5048629905","display_name":"Chunpeng Wu","orcid":"https://orcid.org/0000-0002-3970-8570"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Chunpeng Wu","raw_affiliation_strings":["Electrical and Computer Engineering Department, Duke University, Durham, NC, USA","Duke University,Electrical and Computer Engineering Department,Durham,NC,USA,27708"],"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering Department, Duke University, Durham, NC, USA","institution_ids":["https://openalex.org/I170897317"]},{"raw_affiliation_string":"Duke University,Electrical and Computer Engineering Department,Durham,NC,USA,27708","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100413592","display_name":"Ang Li","orcid":"https://orcid.org/0000-0002-0838-3582"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ang Li","raw_affiliation_strings":["Electrical and Computer Engineering Department, Duke University, Durham, NC, USA","Duke University,Electrical and Computer Engineering Department,Durham,NC,USA,27708"],"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering Department, Duke University, Durham, NC, USA","institution_ids":["https://openalex.org/I170897317"]},{"raw_affiliation_string":"Duke University,Electrical and Computer Engineering Department,Durham,NC,USA,27708","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100339896","display_name":"Bing Li","orcid":"https://orcid.org/0000-0001-6983-7253"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bing Li","raw_affiliation_strings":["Electrical and Computer Engineering Department, Duke University, Durham, NC, USA","Duke University,Electrical and Computer Engineering Department,Durham,NC,USA,27708"],"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering Department, Duke University, Durham, NC, USA","institution_ids":["https://openalex.org/I170897317"]},{"raw_affiliation_string":"Duke University,Electrical and Computer Engineering Department,Durham,NC,USA,27708","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058073627","display_name":"Yiran Chen","orcid":"https://orcid.org/0000-0002-1486-8412"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yiran Chen","raw_affiliation_strings":["Electrical and Computer Engineering Department, Duke University, Durham, NC, USA","Duke University,Electrical and Computer Engineering Department,Durham,NC,USA,27708"],"affiliations":[{"raw_affiliation_string":"Electrical and Computer Engineering Department, Duke University, Durham, NC, USA","institution_ids":["https://openalex.org/I170897317"]},{"raw_affiliation_string":"Duke University,Electrical and Computer Engineering Department,Durham,NC,USA,27708","institution_ids":["https://openalex.org/I170897317"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5048629905"],"corresponding_institution_ids":["https://openalex.org/I170897317"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.23541567,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"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":0.9959999918937683,"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":0.9959999918937683,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9861999750137329,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9818999767303467,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7609416842460632},{"id":"https://openalex.org/keywords/reuse","display_name":"Reuse","score":0.7262673377990723},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6266330480575562},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5741602182388306},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5127254724502563},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4295555651187897},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4108133018016815},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.23702481389045715},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12028834223747253}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7609416842460632},{"id":"https://openalex.org/C206588197","wikidata":"https://www.wikidata.org/wiki/Q846574","display_name":"Reuse","level":2,"score":0.7262673377990723},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6266330480575562},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5741602182388306},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5127254724502563},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4295555651187897},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4108133018016815},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.23702481389045715},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12028834223747253},{"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/C548081761","wikidata":"https://www.wikidata.org/wiki/Q180388","display_name":"Waste management","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/sips47522.2019.9020572","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sips47522.2019.9020572","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.6499999761581421,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W1998299325","https://openalex.org/W2031342017","https://openalex.org/W2115579991","https://openalex.org/W2119112357","https://openalex.org/W2340897893","https://openalex.org/W2342045095","https://openalex.org/W2559767995","https://openalex.org/W2768997245","https://openalex.org/W2796227614","https://openalex.org/W2804337238","https://openalex.org/W2837605352","https://openalex.org/W2891315523","https://openalex.org/W2962977206","https://openalex.org/W2963073614","https://openalex.org/W2963125010","https://openalex.org/W2963709863","https://openalex.org/W2963737762","https://openalex.org/W2963749936","https://openalex.org/W2963857521","https://openalex.org/W2964303162","https://openalex.org/W4232280717","https://openalex.org/W4234674466","https://openalex.org/W6631190155","https://openalex.org/W6638667902","https://openalex.org/W6639824700","https://openalex.org/W6639927594","https://openalex.org/W6677326919","https://openalex.org/W6677477928","https://openalex.org/W6682208247","https://openalex.org/W6684338915","https://openalex.org/W6698200048","https://openalex.org/W6700264148","https://openalex.org/W6702130928","https://openalex.org/W6704559304","https://openalex.org/W6712884540","https://openalex.org/W6725543821","https://openalex.org/W6730316402","https://openalex.org/W6733956904","https://openalex.org/W6735738130","https://openalex.org/W6738460352","https://openalex.org/W6746200960","https://openalex.org/W6746839373","https://openalex.org/W6748097816","https://openalex.org/W6750749703","https://openalex.org/W6752444042","https://openalex.org/W6760506601"],"related_works":["https://openalex.org/W11738893","https://openalex.org/W4972971","https://openalex.org/W9948473","https://openalex.org/W12829028","https://openalex.org/W12803709","https://openalex.org/W8218506","https://openalex.org/W7619760","https://openalex.org/W12219208","https://openalex.org/W929682","https://openalex.org/W5668360"],"abstract_inverted_index":{"Human":[0],"drivers":[1],"learn":[2,56],"driving":[3,11,22,30,37],"skills":[4],"from":[5,28],"both":[6],"regular":[7,21,53,107],"(non-accidental)":[8],"and":[9,85,109,164,187],"accidental":[10,29],"experiences,":[12],"while":[13],"most":[14],"of":[15,102,121,154,167],"current":[16],"self-driving":[17],"research":[18],"focuses":[19],"on":[20,149],"only.":[23],"We":[24],"argue":[25],"that":[26,131,175],"learning":[27],"data":[31,46,54,63,108,111],"is":[32,43,66,80],"necessary":[33],"for":[34,72,95],"robustly":[35],"modeling":[36],"behavior.":[38],"A":[39],"main":[40],"challenge,":[41],"however,":[42],"how":[44],"accident":[45,62,110],"can":[47],"be":[48],"effectively":[49],"used":[50],"together":[51],"with":[52,197],"to":[55,81,128,185],"vehicle":[57,74,169],"motion,":[58],"since":[59],"manually":[60],"labeling":[61],"without":[64],"expertise":[65],"significantly":[67],"difficult.":[68],"Our":[69,152],"proposed":[70],"solution":[71],"robust":[73,168],"motion":[75,170],"learning,":[76],"in":[77],"this":[78],"paper,":[79],"integrate":[82],"layer-level":[83],"discriminability":[84,99],"neuron":[86,122,150],"coverage(neuron-level":[87],"robustness)":[88],"regulariziers":[89,117],"into":[90,135],"an":[91],"unsupervised":[92],"generative":[93],"network":[94,113,159],"video":[96],"prediction.":[97],"Layer-level":[98],"increases":[100],"divergence":[101],"feature":[103],"distribution":[104],"between":[105],"the":[106,179,194],"at":[112],"layers.":[114],"Neuron":[115],"coverage":[116],"enlarge":[118],"interval":[119,137],"span":[120],"activation":[123],"adopted":[124],"by":[125,182],"training":[126,141,191],"samples,":[127],"reduce":[129,157],"probability":[130],"a":[132],"sample":[133],"falls":[134],"untested":[136],"regions.":[138],"To":[139],"accelerate":[140],"process,":[142],"we":[143],"propose":[144],"adaptive":[145],"filter":[146,155],"reuse":[147,156],"based":[148],"coverage.":[151],"strategies":[153],"structural":[158],"parameters,":[160],"encourage":[161],"memory":[162],"reuse,":[163],"guarantee":[165],"effectiveness":[166],"learning.":[171],"Experiments":[172],"results":[173],"show":[174],"our":[176],"model":[177],"improves":[178],"inference":[180],"accuracy":[181,199],"1.1%":[183],"compared":[184],"FCMLSTM,":[186],"cut":[188],"down":[189],"10.2%":[190],"time":[192],"over":[193],"traditional":[195],"method":[196],"negligible":[198],"loss.":[200]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
