{"id":"https://openalex.org/W2605592331","doi":"https://doi.org/10.1109/itsc.2017.8317618","title":"LIDAR-based driving path generation using fully convolutional neural networks","display_name":"LIDAR-based driving path generation using fully convolutional neural networks","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2605592331","doi":"https://doi.org/10.1109/itsc.2017.8317618","mag":"2605592331"},"language":"en","primary_location":{"id":"doi:10.1109/itsc.2017.8317618","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2017.8317618","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5063737919","display_name":"Luca Caltagirone","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":"Luca Caltagirone","raw_affiliation_strings":["Applied Mechanics Department, Chalmers University of Technology, Gothenburg"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Applied Mechanics Department, Chalmers University of Technology, Gothenburg","institution_ids":["https://openalex.org/I66862912"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060252829","display_name":"Mauro Bellone","orcid":"https://orcid.org/0000-0003-3692-0688"},"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":"Mauro Bellone","raw_affiliation_strings":["Applied Mechanics Department, Chalmers University of Technology, Gothenburg"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Applied Mechanics Department, Chalmers University of Technology, Gothenburg","institution_ids":["https://openalex.org/I66862912"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029413988","display_name":"Lennart Svensson","orcid":"https://orcid.org/0000-0003-0206-9186"},"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":"Lennart Svensson","raw_affiliation_strings":["Chalmers University of Technology","Signal and Systems Department"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chalmers University of Technology","institution_ids":["https://openalex.org/I66862912"]},{"raw_affiliation_string":"Signal and Systems Department","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028236337","display_name":"Mattias Wahde","orcid":"https://orcid.org/0000-0001-6679-637X"},"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":"Mattias Wahde","raw_affiliation_strings":["Applied Mechanics Department, Chalmers University of Technology, Gothenburg"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Applied Mechanics Department, Chalmers University of Technology, Gothenburg","institution_ids":["https://openalex.org/I66862912"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I66862912"],"apc_list":null,"apc_paid":null,"fwci":7.5809,"has_fulltext":false,"cited_by_count":55,"citation_normalized_percentile":{"value":0.98741097,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9997000098228455,"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":0.9997000098228455,"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/T10586","display_name":"Robotic Path Planning Algorithms","score":0.9958000183105469,"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"}},{"id":"https://openalex.org/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9955999851226807,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7956570386886597},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7737096548080444},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.7058653831481934},{"id":"https://openalex.org/keywords/inertial-measurement-unit","display_name":"Inertial measurement unit","score":0.6051524877548218},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5903115272521973},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.5300654768943787},{"id":"https://openalex.org/keywords/parsing","display_name":"Parsing","score":0.5286588668823242},{"id":"https://openalex.org/keywords/global-positioning-system","display_name":"Global Positioning System","score":0.5223590135574341},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.45369815826416016},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43086469173431396},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.4221551716327667},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.34514904022216797},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.11651107668876648},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.08563187718391418},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.08198195695877075}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7956570386886597},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7737096548080444},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.7058653831481934},{"id":"https://openalex.org/C79061980","wikidata":"https://www.wikidata.org/wiki/Q941680","display_name":"Inertial measurement unit","level":2,"score":0.6051524877548218},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5903115272521973},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.5300654768943787},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.5286588668823242},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.5223590135574341},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.45369815826416016},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43086469173431396},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.4221551716327667},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.34514904022216797},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.11651107668876648},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.08563187718391418},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.08198195695877075},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/itsc.2017.8317618","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2017.8317618","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"},{"id":"pmh:oai:research.chalmers.se:507296","is_oa":false,"landing_page_url":"https://research.chalmers.se/en/publication/9c6fccc9-e9d5-4839-b904-7876e535fc1d","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":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321030","display_name":"VINNOVA","ror":"https://ror.org/01kd5m353"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W360623563","https://openalex.org/W1903029394","https://openalex.org/W2115579991","https://openalex.org/W2119112357","https://openalex.org/W2167224731","https://openalex.org/W2340017589","https://openalex.org/W2342840547","https://openalex.org/W2559767995","https://openalex.org/W2596750703","https://openalex.org/W2919115771","https://openalex.org/W2952236454","https://openalex.org/W2963840672","https://openalex.org/W4299603580","https://openalex.org/W6684338915","https://openalex.org/W6696085341"],"related_works":["https://openalex.org/W4319317934","https://openalex.org/W2901265155","https://openalex.org/W2956374172","https://openalex.org/W4319837668","https://openalex.org/W4308071650","https://openalex.org/W3188333020","https://openalex.org/W1964041166","https://openalex.org/W4293094720","https://openalex.org/W2739701376","https://openalex.org/W2562256921"],"abstract_inverted_index":{"In":[0],"this":[1,132],"work,":[2],"a":[3,30,101,112],"novel":[4],"learning-based":[5],"approach":[6],"has":[7],"been":[8],"developed":[9],"to":[10,38,67,125,157],"generate":[11],"driving":[12,23,47,90],"paths":[13,121],"by":[14,73,151],"integrating":[15],"LIDAR":[16],"point":[17],"clouds,":[18],"GPS-IMU":[19],"information,":[20],"and":[21,42,49,122,148,160],"Google":[22],"directions.":[24],"The":[25,77,127],"system":[26,138],"is":[27,51],"based":[28],"on":[29],"fully":[31,78],"convolutional":[32,79],"neural":[33,80],"network":[34,81],"that":[35,50,135,154],"jointly":[36],"learns":[37],"carry":[39],"out":[40],"perception":[41],"path":[43],"generation":[44],"from":[45],"real-world":[46],"sequences":[48],"trained":[52,82],"using":[53,83],"automatically":[54],"generated":[55],"training":[56],"examples.":[57],"Several":[58],"combinations":[59],"of":[60,97,103,105,115],"input":[61],"data":[62],"were":[63],"tested":[64],"in":[65,131],"order":[66],"assess":[68],"the":[69,85,93,117,136,142,162],"performance":[70],"gain":[71],"provided":[72],"specific":[74],"information":[75],"modalities.":[76],"all":[84],"available":[86],"sensors":[87],"together":[88],"with":[89],"directions":[91],"achieved":[92],"best":[94],"MaxF":[95],"score":[96],"88.13%":[98],"when":[99],"considering":[100,111],"region":[102,114],"interest":[104],"60":[106,108],"\u00d7":[107],"meters.":[109],"By":[110],"smaller":[113],"interest,":[116],"agreement":[118],"between":[119,144],"predicted":[120],"ground-truth":[123],"increased":[124],"92.60%.":[126],"positive":[128],"results":[129],"obtained":[130],"work":[133],"indicate":[134],"proposed":[137],"may":[139],"help":[140],"fill":[141],"gap":[143],"low-level":[145],"scene":[146],"parsing":[147],"behavior-reflex":[149],"approaches":[150],"generating":[152],"outputs":[153],"are":[155],"close":[156],"vehicle":[158],"control":[159],"at":[161],"same":[163],"time":[164],"human-interpretable.":[165]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":9},{"year":2018,"cited_by_count":7},{"year":2017,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
