{"id":"https://openalex.org/W3039634051","doi":"https://doi.org/10.1109/vtc2020-spring48590.2020.9128855","title":"A Scheme on Pedestrian Detection using Multi-Sensor Data Fusion for Smart Roads","display_name":"A Scheme on Pedestrian Detection using Multi-Sensor Data Fusion for Smart Roads","publication_year":2020,"publication_date":"2020-05-01","ids":{"openalex":"https://openalex.org/W3039634051","doi":"https://doi.org/10.1109/vtc2020-spring48590.2020.9128855","mag":"3039634051"},"language":"en","primary_location":{"id":"doi:10.1109/vtc2020-spring48590.2020.9128855","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtc2020-spring48590.2020.9128855","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)","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/A5100460905","display_name":"Hui Wang","orcid":"https://orcid.org/0000-0003-1863-4813"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hui Wang","raw_affiliation_strings":["State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, China","State Key Laboratory of Integrated Services Networks, Xidian University, Xi&#x2019","an, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"State Key Laboratory of Integrated Services Networks, Xidian University, Xi&#x2019","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"an, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015340798","display_name":"Changle Li","orcid":"https://orcid.org/0000-0003-2568-8908"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changle Li","raw_affiliation_strings":["State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, China","an, China","State Key Laboratory of Integrated Services Networks, Xidian University, Xi&#x2019"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"an, China","institution_ids":[]},{"raw_affiliation_string":"State Key Laboratory of Integrated Services Networks, Xidian University, Xi&#x2019","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100446948","display_name":"Yao Zhang","orcid":"https://orcid.org/0000-0001-8825-5718"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yao Zhang","raw_affiliation_strings":["State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, China","an, China","State Key Laboratory of Integrated Services Networks, Xidian University, Xi&#x2019"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"an, China","institution_ids":[]},{"raw_affiliation_string":"State Key Laboratory of Integrated Services Networks, Xidian University, Xi&#x2019","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100950051","display_name":"Zhao Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhao Liu","raw_affiliation_strings":["State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, China","State Key Laboratory of Integrated Services Networks, Xidian University, Xi&#x2019","an, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"State Key Laboratory of Integrated Services Networks, Xidian University, Xi&#x2019","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"an, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045332200","display_name":"Yilong Hui","orcid":"https://orcid.org/0000-0001-5543-2669"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yilong Hui","raw_affiliation_strings":["State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, China","State Key Laboratory of Integrated Services Networks, Xidian University, Xi&#x2019","an, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"State Key Laboratory of Integrated Services Networks, Xidian University, Xi&#x2019","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"an, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036000787","display_name":"Guoqiang Mao","orcid":"https://orcid.org/0000-0002-3598-4949"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guoqiang Mao","raw_affiliation_strings":["State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, China","an, China","State Key Laboratory of Integrated Services Networks, Xidian University, Xi&#x2019"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"an, China","institution_ids":[]},{"raw_affiliation_string":"State Key Laboratory of Integrated Services Networks, Xidian University, Xi&#x2019","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100460905"],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":0.1303,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.48788817,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":"4","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.995199978351593,"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"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.995199978351593,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9929999709129333,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9878000020980835,"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/fuse","display_name":"Fuse (electrical)","score":0.6532623767852783},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.6489781141281128},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6186861395835876},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.5848084688186646},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5028988718986511},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.4721667468547821},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.42516767978668213},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.38460105657577515},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.30902615189552307},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.2329351007938385},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.20771288871765137},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.14222919940948486},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.09628552198410034}],"concepts":[{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.6532623767852783},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.6489781141281128},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6186861395835876},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.5848084688186646},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5028988718986511},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.4721667468547821},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.42516767978668213},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.38460105657577515},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.30902615189552307},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.2329351007938385},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.20771288871765137},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.14222919940948486},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.09628552198410034},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/vtc2020-spring48590.2020.9128855","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vtc2020-spring48590.2020.9128855","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)","raw_type":"proceedings-article"},{"id":"pmh:oai:opus.lib.uts.edu.au:10453/149052","is_oa":false,"landing_page_url":"http://hdl.handle.net/10453/149052","pdf_url":null,"source":{"id":"https://openalex.org/S4306401357","display_name":"UTS ePRESS (University of Technology Sydney)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I114017466","host_organization_name":"University of Technology Sydney","host_organization_lineage":["https://openalex.org/I114017466"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Proceeding"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","score":0.4300000071525574,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W2031454541","https://openalex.org/W2625848437","https://openalex.org/W2750840446","https://openalex.org/W2753473674","https://openalex.org/W2795813178","https://openalex.org/W2801227907","https://openalex.org/W2803343971","https://openalex.org/W2808437786","https://openalex.org/W2895922401","https://openalex.org/W2942520659","https://openalex.org/W6744026318","https://openalex.org/W6761874385"],"related_works":["https://openalex.org/W2354322770","https://openalex.org/W3000097931","https://openalex.org/W4237547500","https://openalex.org/W1570848052","https://openalex.org/W2373192430","https://openalex.org/W4239268388","https://openalex.org/W1537496349","https://openalex.org/W2972620127","https://openalex.org/W2981141433","https://openalex.org/W2154495931"],"abstract_inverted_index":{"Transforming":[0],"our":[1,144],"roads":[2,5],"into":[3],"smart":[4,56,157],"is":[6,88,148,160,166,181],"an":[7,52,137],"indispensable":[8],"step":[9],"towards":[10],"future":[11],"self-driving":[12],"systems,":[13],"and":[14,23,80,95,113],"therefore":[15],"has":[16],"drawn":[17],"increasing":[18],"attention":[19],"from":[20],"both":[21],"academia":[22],"industry.":[24],"To":[25],"this":[26,28],"end,":[27],"paper":[29],"develops":[30],"a":[31,45,167,178],"novel":[32],"cost-effective":[33],"IoT-based":[34,151],"target":[35,98],"detection":[36,64,140,155,172],"system":[37,173],"utilizing":[38],"the":[39,60,92,104,124,131,149,175],"multi-sensor":[40,84],"data":[41,85,94],"fusion":[42,86],"technology":[43],"with":[44,136],"particular":[46],"focus":[47],"on":[48,118],"pedestrian":[49,63,154,171],"detection,":[50],"as":[51],"important":[53],"component":[54],"of":[55,68,126,134,156,177],"road":[57],"system.":[58],"Particularly,":[59],"developed":[61,89],"intelligent":[62],"module":[65],"(${i}$PDM)":[66],"consists":[67],"three":[69],"major":[70],"sensors,":[71],"i.e.,":[72],"Doppler":[73],"microwave":[74],"radar":[75],"sensor,":[76],"passive":[77],"infrared":[78],"(PIR),":[79],"geomagnetic":[81],"sensor.":[82],"A":[83],"algorithm":[87],"to":[90,109,122,143,162,190],"fuse":[91],"sensor":[93],"achieves":[96],"reliable":[97],"detection.":[99],"After":[100],"that,":[101],"${i}$PDM":[102,135,147,165,180],"sends":[103],"relevant":[105],"warning":[106],"signal":[107],"wirelessly":[108],"nearby":[110],"base":[111],"station":[112],"vehicles.":[114],"Experiments":[115],"are":[116],"conducted":[117],"real":[119],"traffic":[120],"environment":[121],"evaluate":[123],"performance":[125],"${i}$PDM.":[127],"The":[128],"results":[129],"validate":[130],"high":[132],"reliability":[133],"average":[138],"91.7%":[139],"accuracy.":[141],"Moreover,":[142],"best":[145],"knowledge,":[146],"first":[150],"implementation":[152],"for":[153],"roads.":[158],"It":[159],"necessary":[161],"highlight":[163],"that":[164],"low-cost,":[168],"low-power,":[169],"wide-coverage":[170],"where":[174],"cost":[176],"single":[179],"only":[182],"US":[183],"$":[184],"30,":[185],"which":[186],"makes":[187],"it":[188],"suitable":[189],"large-scale":[191],"deployment.":[192]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}