{"id":"https://openalex.org/W2884115443","doi":null,"title":"Deep path planning using images and object data","display_name":"Deep path planning using images and object data","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2884115443","mag":"2884115443"},"language":"en","primary_location":{"id":"pmh:oai:publications.lib.chalmers.se:255340","doi":null,"is_oa":true,"landing_page_url":"http://studentarbeten.chalmers.se/publication/255340-deep-path-planning-using-images-and-object-data","pdf_url":"http://studentarbeten.chalmers.se/publication/255340-deep-path-planning-using-images-and-object-data","source":{"id":"https://openalex.org/S4377196470","display_name":"Chalmers Publication Library (Chalmers University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"Text.Thesis.Masters.H"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://studentarbeten.chalmers.se/publication/255340-deep-path-planning-using-images-and-object-data","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5089212587","display_name":"Maria Bergqvist","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Maria Bergqvist","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5010140870","display_name":"Oskar R\u00f6dholm","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Oskar R\u00f6dholm","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"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":0.9829000234603882,"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":0.9829000234603882,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.009700000286102295,"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/T10586","display_name":"Robotic Path Planning Algorithms","score":0.00279999990016222,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/autonomous-vehicles","display_name":"autonomous vehicles","score":0.8659999966621399},{"id":"https://openalex.org/keywords/path-planning","display_name":"path planning","score":0.9129999876022339},{"id":"https://openalex.org/keywords/imitation-learning","display_name":"imitation learning","score":0.9700000286102295},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"sensor fusion","score":0.6850000023841858},{"id":"https://openalex.org/keywords/driving-safety","display_name":"driving safety","score":0.4180000126361847},{"id":"https://openalex.org/keywords/robustness","display_name":"robustness","score":0.44999998807907104}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5702252388000488},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5324286818504333},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4902551472187042},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4204850196838379},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.41254308819770813},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33202821016311646},{"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":"pmh:oai:publications.lib.chalmers.se:255340","doi":null,"is_oa":true,"landing_page_url":"http://studentarbeten.chalmers.se/publication/255340-deep-path-planning-using-images-and-object-data","pdf_url":"http://studentarbeten.chalmers.se/publication/255340-deep-path-planning-using-images-and-object-data","source":{"id":"https://openalex.org/S4377196470","display_name":"Chalmers Publication Library (Chalmers University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"Text.Thesis.Masters.H"},{"id":"mag:2884115443","doi":null,"is_oa":false,"landing_page_url":"https://odr.chalmers.se/bitstream/20.500.12380/255340/1/255340.pdf","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"pmh:oai:publications.lib.chalmers.se:255340","doi":null,"is_oa":true,"landing_page_url":"http://studentarbeten.chalmers.se/publication/255340-deep-path-planning-using-images-and-object-data","pdf_url":"http://studentarbeten.chalmers.se/publication/255340-deep-path-planning-using-images-and-object-data","source":{"id":"https://openalex.org/S4377196470","display_name":"Chalmers Publication Library (Chalmers University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"Text.Thesis.Masters.H"},"sustainable_development_goals":[{"score":0.428600013256073,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2884115443.pdf","grobid_xml":"https://content.openalex.org/works/W2884115443.grobid-xml"},"referenced_works_count":15,"referenced_works":["https://openalex.org/W1582774210","https://openalex.org/W1585377561","https://openalex.org/W1686810756","https://openalex.org/W1904365287","https://openalex.org/W1995341919","https://openalex.org/W2112796928","https://openalex.org/W2342840547","https://openalex.org/W2559767995","https://openalex.org/W2729352406","https://openalex.org/W2745467776","https://openalex.org/W2949650786","https://openalex.org/W2950179405","https://openalex.org/W2952230511","https://openalex.org/W2964121744","https://openalex.org/W4232280717"],"related_works":["https://openalex.org/W7212447269","https://openalex.org/W2981513425","https://openalex.org/W3142386621","https://openalex.org/W3119918579","https://openalex.org/W4308659735","https://openalex.org/W3091329246","https://openalex.org/W4415995142","https://openalex.org/W2897340412","https://openalex.org/W4380994034","https://openalex.org/W4322731264"],"abstract_inverted_index":{"Autonomous":[0],"vehicles":[1,42,63,159],"are":[2,195],"currently":[3],"being":[4],"heavily":[5],"researched":[6],"with":[7],"the":[8,47,82,87,108,111,116,129,141,155,171,177,184,191],"purpose":[9,48],"of":[10,67,78,86,110,122],"finding":[11],"a":[12,16,96,120,201],"manner":[13],"in":[14,40,160,197],"which":[15,29,35,99],"vehicle":[17,112],"can":[18,55],"drive":[19],"safely.An":[20],"approach":[21],"for":[22,61,157],"doing":[23],"this":[24,45],"is":[25,32,49,90,147],"imitation":[26,53],"learning":[27,54,152],"by":[28],"human":[30],"behavior":[31],"imitated,":[33],"and":[34,84,94,106,204],"recently":[36],"has":[37],"found":[38],"success":[39],"steering":[41],"using":[43,64,179],"images.For":[44],"thesis,":[46],"to":[50,58,72,92,127,149,153,170,199],"investigate":[51,73],"whether":[52],"be":[56],"applied":[57],"path":[59,109,156],"planning":[60],"autonomous":[62,158],"several":[65,104],"sources":[66,105],"input":[68,101,169],"data.We":[69],"also":[70,163],"want":[71],"if":[74],"merging":[75,190],"different":[76],"types":[77],"information":[79],"will":[80],"improve":[81,176],"safety":[83],"robustness":[85],"vehicle.The":[88],"aim":[89],"therefore":[91],"design":[93],"implement":[95],"neural":[97,123],"network":[98,124],"merges":[100],"data":[102,133,182],"from":[103,134],"predicts":[107],"5":[113],"seconds":[114],"into":[115],"future.This":[117],"thesis":[118],"compares":[119],"variety":[121],"models":[125,131],"used":[126],"predict":[128,154],"path.Some":[130],"use":[132,150],"only":[135,180],"one":[136],"source,":[137],"while":[138],"other":[139],"merge":[140],"data.The":[142],"results":[143],"show":[144,164],"that":[145,165],"it":[146],"possible":[148],"deep":[151],"some":[161],"situations.They":[162],"including":[166],"images":[167],"as":[168,187,189],"model":[172],"does":[173],"not":[174],"significantly":[175],"performance.Instead,":[178],"object":[181],"make":[183],"networks":[185],"performs":[186],"good":[188],"inputs.However,":[192],"more":[193,202],"research":[194],"needed":[196],"order":[198],"find":[200],"accurate":[203],"stable":[205],"model.":[206]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2018-08-03T00:00:00"}