{"id":"https://openalex.org/W7135060047","doi":"https://doi.org/10.1109/tits.2026.3670490","title":"A Novel Driving Risk Assessment Method Based on Traffic Scene Understanding and Causal Reasoning","display_name":"A Novel Driving Risk Assessment Method Based on Traffic Scene Understanding and Causal Reasoning","publication_year":2026,"publication_date":"2026-03-11","ids":{"openalex":"https://openalex.org/W7135060047","doi":"https://doi.org/10.1109/tits.2026.3670490"},"language":null,"primary_location":{"id":"doi:10.1109/tits.2026.3670490","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2026.3670490","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/A5128863337","display_name":"Peng Ping","orcid":null},"institutions":[{"id":"https://openalex.org/I199305430","display_name":"Nantong University","ror":"https://ror.org/02afcvw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I199305430"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Ping","raw_affiliation_strings":["School of Transportation and Civil Engineering, Nantong University, Nantong, China"],"raw_orcid":"https://orcid.org/0009-0002-6300-5848","affiliations":[{"raw_affiliation_string":"School of Transportation and Civil Engineering, Nantong University, Nantong, China","institution_ids":["https://openalex.org/I199305430"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113272891","display_name":"Qida Yao","orcid":null},"institutions":[{"id":"https://openalex.org/I199305430","display_name":"Nantong University","ror":"https://ror.org/02afcvw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I199305430"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qida Yao","raw_affiliation_strings":["School of Transportation and Civil Engineering, Nantong University, Nantong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Transportation and Civil Engineering, Nantong University, Nantong, China","institution_ids":["https://openalex.org/I199305430"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090540721","display_name":"Quan Shi","orcid":"https://orcid.org/0000-0002-4614-1023"},"institutions":[{"id":"https://openalex.org/I199305430","display_name":"Nantong University","ror":"https://ror.org/02afcvw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I199305430"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Quan Shi","raw_affiliation_strings":["School of Transportation and Civil Engineering, Nantong University, Nantong, China"],"raw_orcid":"https://orcid.org/0000-0002-4614-1023","affiliations":[{"raw_affiliation_string":"School of Transportation and Civil Engineering, Nantong University, Nantong, China","institution_ids":["https://openalex.org/I199305430"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082878649","display_name":"Yeqin Shao","orcid":"https://orcid.org/0000-0001-7868-2767"},"institutions":[{"id":"https://openalex.org/I199305430","display_name":"Nantong University","ror":"https://ror.org/02afcvw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I199305430"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yeqin Shao","raw_affiliation_strings":["School of Transportation and Civil Engineering, Nantong University, Nantong, China"],"raw_orcid":"https://orcid.org/0000-0001-7868-2767","affiliations":[{"raw_affiliation_string":"School of Transportation and Civil Engineering, Nantong University, Nantong, China","institution_ids":["https://openalex.org/I199305430"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006816662","display_name":"Yingyan Hou","orcid":"https://orcid.org/0000-0002-2789-8581"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210137199","display_name":"Aerospace Information Research Institute","ror":"https://ror.org/0419fj215","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210137199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingyan Hou","raw_affiliation_strings":["Chinese Academy of Sciences, Aerospace Information Research Institute, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-2789-8581","affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, Aerospace Information Research Institute, Beijing, China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210137199"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026001351","display_name":"Wanxuan Lu","orcid":"https://orcid.org/0000-0003-4612-508X"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210137199","display_name":"Aerospace Information Research Institute","ror":"https://ror.org/0419fj215","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210137199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wanxuan Lu","raw_affiliation_strings":["Chinese Academy of Sciences, Aerospace Information Research Institute, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-4612-508X","affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, Aerospace Information Research Institute, Beijing, China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210137199"]}]},{"author_position":"last","author":{"id":null,"display_name":"Weiping Ding","orcid":"https://orcid.org/0000-0002-3180-7347"},"institutions":[{"id":"https://openalex.org/I199305430","display_name":"Nantong University","ror":"https://ror.org/02afcvw97","country_code":"CN","type":"education","lineage":["https://openalex.org/I199305430"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiping Ding","raw_affiliation_strings":["School of Artificial Intelligence and Computer Science, Nantong University, Nantong, China"],"raw_orcid":"https://orcid.org/0000-0002-3180-7347","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence and Computer Science, Nantong University, Nantong, China","institution_ids":["https://openalex.org/I199305430"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.23057421,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"27","issue":"7","first_page":"7920","last_page":"7935"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9591000080108643,"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":0.9591000080108643,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.004900000058114529,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.004800000227987766,"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/graph","display_name":"Graph","score":0.5378999710083008},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4875999987125397},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4348999857902527},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4074999988079071},{"id":"https://openalex.org/keywords/completeness","display_name":"Completeness (order theory)","score":0.4043000042438507},{"id":"https://openalex.org/keywords/advanced-driver-assistance-systems","display_name":"Advanced driver assistance systems","score":0.39399999380111694},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3481999933719635},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.33709999918937683},{"id":"https://openalex.org/keywords/task-analysis","display_name":"Task analysis","score":0.3138999938964844}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6766999959945679},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6062999963760376},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5479000210762024},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5378999710083008},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4875999987125397},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4348999857902527},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4074999988079071},{"id":"https://openalex.org/C17231256","wikidata":"https://www.wikidata.org/wiki/Q5156540","display_name":"Completeness (order theory)","level":2,"score":0.4043000042438507},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.39399999380111694},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3481999933719635},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.33709999918937683},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.3138999938964844},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.3086000084877014},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.3037000000476837},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.3034000098705292},{"id":"https://openalex.org/C115086926","wikidata":"https://www.wikidata.org/wiki/Q17004651","display_name":"Causal reasoning","level":3,"score":0.3003999888896942},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.29919999837875366},{"id":"https://openalex.org/C12174686","wikidata":"https://www.wikidata.org/wiki/Q1058438","display_name":"Risk assessment","level":2,"score":0.29789999127388},{"id":"https://openalex.org/C193221554","wikidata":"https://www.wikidata.org/wiki/Q5153664","display_name":"Commonsense reasoning","level":2,"score":0.29010000824928284},{"id":"https://openalex.org/C179372163","wikidata":"https://www.wikidata.org/wiki/Q1406181","display_name":"Scene graph","level":3,"score":0.2874999940395355},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.2827000021934509},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2824999988079071},{"id":"https://openalex.org/C3017944768","wikidata":"https://www.wikidata.org/wiki/Q1450463","display_name":"Poison control","level":2,"score":0.2766999900341034},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.26969999074935913},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.2547999918460846},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25220000743865967}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2026.3670490","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2026.3670490","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":[],"awards":[{"id":"https://openalex.org/G1968828270","display_name":null,"funder_award_id":"52442218","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4286351655","display_name":null,"funder_award_id":"U2433216","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5507196296","display_name":null,"funder_award_id":"52202496","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"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"understanding":[1,68],"of":[2,9,31,67,98,127,140],"complex":[3,36,111,141],"driving":[4,76,82],"scenarios":[5],"and":[6,23,34,85,123,154,161,181,188],"the":[7,28,69,81,90,95,103,125,138,171],"prediction":[8,165],"potential":[10,75,108],"risks":[11,109],"have":[12],"always":[13],"been":[14],"critical":[15],"prerequisites":[16],"for":[17,44],"autonomous":[18,45],"vehicles":[19,46],"to":[20,47,54,93,106,121,157],"generate":[21],"safe":[22],"reliable":[24],"behavioral":[25],"decisions.":[26],"However,":[27],"uncertain":[29],"states":[30],"multiple":[32],"objects":[33],"their":[35],"interactions":[37],"in":[38,110],"intricate":[39],"scenes":[40],"make":[41],"it":[42],"difficult":[43],"extract":[48],"sufficient":[49],"information":[50],"from":[51],"representation":[52],"learning":[53,148],"comprehend":[55],"road":[56,70],"scenarios.":[57,112],"To":[58],"address":[59],"this,":[60],"this":[61],"paper":[62],"proposes":[63],"a":[64,114,147],"novel":[65],"method":[66,173],"scene":[71,91,142],"aimed":[72],"at":[73],"identifying":[74,107],"risks.":[77],"Firstly,":[78],"we":[79,145],"integrate":[80],"risk":[83,164],"field":[84],"individual":[86],"behavior":[87],"features":[88,160],"into":[89],"graph":[92,152],"enhance":[94],"dynamic":[96],"characteristics":[97],"perceived":[99],"targets,":[100],"thereby":[101],"improving":[102],"system\u2019s":[104],"sensitivity":[105],"Subsequently,":[113],"causal":[115],"reasoning":[116],"completion":[117],"module":[118],"is":[119],"introduced":[120],"infer":[122],"reconstruct":[124],"state":[126],"traffic":[128],"entities":[129],"when":[130],"they":[131],"are":[132],"occluded":[133],"or":[134],"undetected,":[135],"effectively":[136],"enhancing":[137],"completeness":[139],"perception.":[143],"Finally,":[144],"incorporate":[146],"framework":[149],"that":[150,170],"combines":[151],"convolution":[153],"temporal":[155],"transformers":[156],"capture":[158],"node":[159],"relationships,":[162],"outputting":[163],"results.":[166],"Experimental":[167],"results":[168],"demonstrate":[169],"proposed":[172],"outperforms":[174],"baseline":[175],"approaches":[176],"on":[177],"both":[178],"public":[179],"datasets":[180],"self-constructed":[182],"occlusion":[183],"datasets,":[184],"validating":[185],"its":[186],"effectiveness":[187],"superiority.":[189]},"counts_by_year":[],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2026-03-13T00:00:00"}