{"id":"https://openalex.org/W2921259920","doi":"https://doi.org/10.5220/0007682502320242","title":"Using Recurrent Neural Networks for Action and Intention Recognition of Car Drivers","display_name":"Using Recurrent Neural Networks for Action and Intention Recognition of Car Drivers","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2921259920","doi":"https://doi.org/10.5220/0007682502320242","mag":"2921259920"},"language":"en","primary_location":{"id":"doi:10.5220/0007682502320242","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0007682502320242","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.5220/0007682502320242","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5079880833","display_name":"Martin Torstensson","orcid":null},"institutions":[{"id":"https://openalex.org/I66862912","display_name":"Chalmers University of Technology","ror":"https://ror.org/040wg7k59","country_code":"SE","type":"education","lineage":["https://openalex.org/I66862912"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Martin Torstensson","raw_affiliation_strings":["Chalmers University of Technology, Gothenburg and Sweden, --- Select a Country ---"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chalmers University of Technology, Gothenburg and Sweden, --- Select a Country ---","institution_ids":["https://openalex.org/I66862912"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049923108","display_name":"Boris Dur\u00e1n","orcid":null},"institutions":[{"id":"https://openalex.org/I2799819292","display_name":"Lindholmen Science Park","ror":"https://ror.org/00zh7c888","country_code":"SE","type":"archive","lineage":["https://openalex.org/I2799819292"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Boris Duran","raw_affiliation_strings":["RISE Viktoria, Lindholmspiren 3A, SE 417 56 Gothenburg and Sweden, --- Select a Country ---"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RISE Viktoria, Lindholmspiren 3A, SE 417 56 Gothenburg and Sweden, --- Select a Country ---","institution_ids":["https://openalex.org/I2799819292"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068474724","display_name":"Cristofer Englund","orcid":"https://orcid.org/0000-0002-1043-8773"},"institutions":[{"id":"https://openalex.org/I2799819292","display_name":"Lindholmen Science Park","ror":"https://ror.org/00zh7c888","country_code":"SE","type":"archive","lineage":["https://openalex.org/I2799819292"]},{"id":"https://openalex.org/I746986","display_name":"Halmstad University","ror":"https://ror.org/03h0qfp10","country_code":"SE","type":"education","lineage":["https://openalex.org/I746986"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Cristofer Englund","raw_affiliation_strings":["RISE Viktoria, Lindholmspiren 3A, SE 417 56 Gothenburg, Sweden, Center for Applied Intelligent Systems Research (CAISR), Halmstad University, SE 301 18 Halmstad and Sweden, --- Select a Country ---","RISE Viktoria, Lindholmspiren 3A, SE 417 56 Gothenburg, Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RISE Viktoria, Lindholmspiren 3A, SE 417 56 Gothenburg, Sweden, Center for Applied Intelligent Systems Research (CAISR), Halmstad University, SE 301 18 Halmstad and Sweden, --- Select a Country ---","institution_ids":["https://openalex.org/I746986"]},{"raw_affiliation_string":"RISE Viktoria, Lindholmspiren 3A, SE 417 56 Gothenburg, Sweden","institution_ids":["https://openalex.org/I2799819292"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.0933,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.86104862,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"232","last_page":"242"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10525","display_name":"Human-Automation Interaction and Safety","score":0.9682999849319458,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10525","display_name":"Human-Automation Interaction and Safety","score":0.9682999849319458,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9628999829292297,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9577000141143799,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7764002084732056},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6729803681373596},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6389285326004028},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.5966833233833313},{"id":"https://openalex.org/keywords/warning-system","display_name":"Warning system","score":0.5597904324531555},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.49590954184532166},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4698164761066437},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.4669545590877533},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.44583818316459656},{"id":"https://openalex.org/keywords/long-short-term-memory","display_name":"Long short term memory","score":0.4404422342777252},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3587660789489746},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.12577345967292786},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.08798354864120483}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7764002084732056},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6729803681373596},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6389285326004028},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.5966833233833313},{"id":"https://openalex.org/C29825287","wikidata":"https://www.wikidata.org/wiki/Q1427940","display_name":"Warning system","level":2,"score":0.5597904324531555},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.49590954184532166},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4698164761066437},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.4669545590877533},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.44583818316459656},{"id":"https://openalex.org/C133488467","wikidata":"https://www.wikidata.org/wiki/Q6673524","display_name":"Long short term memory","level":4,"score":0.4404422342777252},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3587660789489746},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.12577345967292786},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.08798354864120483},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.5220/0007682502320242","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0007682502320242","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods","raw_type":"proceedings-article"},{"id":"pmh:oai:research.chalmers.se:538152","is_oa":false,"landing_page_url":"https://research.chalmers.se/en/publication/538152","pdf_url":null,"source":{"id":"https://openalex.org/S4306402469","display_name":"Chalmers Research (Chalmers University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I66862912","host_organization_name":"Chalmers University of Technology","host_organization_lineage":["https://openalex.org/I66862912"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":null}],"best_oa_location":{"id":"doi:10.5220/0007682502320242","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0007682502320242","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.5199999809265137}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1789187189","https://openalex.org/W1904693176","https://openalex.org/W2064044806","https://openalex.org/W2117539524","https://openalex.org/W2131774270","https://openalex.org/W2136831404","https://openalex.org/W2156303437","https://openalex.org/W2212765426","https://openalex.org/W2324736398","https://openalex.org/W2402302915","https://openalex.org/W2533823693","https://openalex.org/W2594496282","https://openalex.org/W2624162241","https://openalex.org/W2783256039","https://openalex.org/W2809254203","https://openalex.org/W2910815021","https://openalex.org/W2963692464","https://openalex.org/W4298947612"],"related_works":["https://openalex.org/W2912153778","https://openalex.org/W4387163678","https://openalex.org/W4288108708","https://openalex.org/W2973430807","https://openalex.org/W4385280324","https://openalex.org/W2984436043","https://openalex.org/W4390245176","https://openalex.org/W2912831041","https://openalex.org/W2890685186","https://openalex.org/W3173606726"],"abstract_inverted_index":{"Traffic":[0],"situations":[1],"leading":[2],"up":[3],"to":[4,9,60,109],"accidents":[5],"have":[6,33],"been":[7,34],"shown":[8],"be":[10,52,75],"greatly":[11],"affected":[12],"by":[13],"human":[14],"errors.":[15],"To":[16],"reduce":[17],"these":[18],"errors,":[19],"warning":[20,50,73],"systems":[21],"such":[22],"as":[23,54,56],"Driver":[24],"Alert":[25],"Control,":[26],"Collision":[27],"Warning":[28,32],"and":[29,80,100,111,139],"Lane":[30],"Departure":[31],"introduced.":[35],"However,":[36],"there":[37],"is":[38],"still":[39],"room":[40],"for":[41],"improvement,":[42],"both":[43],"regarding":[44],"the":[45,57,78,81,84,130,133,136],"timing":[46],"of":[47,83,95,115,126,132,142,151],"when":[48,71],"a":[49,62,72,96,101,116,119,148],"should":[51,74],"given":[53,76],"well":[55],"time":[58],"needed":[59],"detect":[61,110],"hazardous":[63],"situation":[64],"in":[65,135],"advance.":[66],"Two":[67],"factors":[68],"that":[69],"affect":[70],"are":[77],"environment":[79],"actions":[82,114,131],"driver.":[85],"This":[86],"study":[87],"proposes":[88],"an":[89,124,140],"artificial":[90],"neural":[91,98,103],"network-based":[92],"approach":[93],"consisting":[94],"convolutional":[97],"network":[99,104,122],"recurrent":[102],"with":[105,147],"long":[106],"short-term":[107],"memory":[108],"predict":[112],"different":[113],"driver":[117,134],"inside":[118],"vehicle.":[120],"The":[121],"achieved":[123],"accuracy":[125,141],"84%":[127],"while":[128],"predicting":[129],"next":[137],"frame,":[138],"58%":[143],"20":[144],"frames":[145,154],"ahead":[146],"sampling":[149],"rate":[150],"approximately":[152],"30":[153],"per":[155],"second.":[156]},"counts_by_year":[{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":1}],"updated_date":"2026-08-27T14:10:00.468798","created_date":"2025-10-10T00:00:00"}
