{"id":"https://openalex.org/W2631888524","doi":"https://doi.org/10.1109/icra.2018.8460203","title":"Pedestrian Prediction by Planning Using Deep Neural Networks","display_name":"Pedestrian Prediction by Planning Using Deep Neural Networks","publication_year":2018,"publication_date":"2018-05-01","ids":{"openalex":"https://openalex.org/W2631888524","doi":"https://doi.org/10.1109/icra.2018.8460203","mag":"2631888524"},"language":"en","primary_location":{"id":"doi:10.1109/icra.2018.8460203","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2018.8460203","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Robotics and Automation (ICRA)","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/A5022113943","display_name":"Eike Rehder","orcid":"https://orcid.org/0000-0002-6255-0724"},"institutions":[{"id":"https://openalex.org/I891521709","display_name":"Daimler (Germany)","ror":"https://ror.org/00m0j3d84","country_code":"DE","type":"company","lineage":["https://openalex.org/I891521709"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Eike Rehder","raw_affiliation_strings":["Environment Perception, Daimler R&D, Sindelfingen, Germany","Daimler R&D, Environment Perception, Sindelfingen, Germany"],"affiliations":[{"raw_affiliation_string":"Environment Perception, Daimler R&D, Sindelfingen, Germany","institution_ids":["https://openalex.org/I891521709"]},{"raw_affiliation_string":"Daimler R&D, Environment Perception, Sindelfingen, Germany","institution_ids":["https://openalex.org/I891521709"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102889005","display_name":"Florian Wirth","orcid":"https://orcid.org/0009-0000-2219-4393"},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Florian Wirth","raw_affiliation_strings":["Karlsruhe Institute of Technology (KIT), Institute of Measurement and Control Systems, Karlsruhe, Germany","Institute of Measurement and Control Systems, Karlsruhe Institute of Technology (KIT), Karlsruhe, 76131, Germany"],"affiliations":[{"raw_affiliation_string":"Karlsruhe Institute of Technology (KIT), Institute of Measurement and Control Systems, Karlsruhe, Germany","institution_ids":["https://openalex.org/I102335020"]},{"raw_affiliation_string":"Institute of Measurement and Control Systems, Karlsruhe Institute of Technology (KIT), Karlsruhe, 76131, Germany","institution_ids":["https://openalex.org/I102335020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008189468","display_name":"Martin Lauer","orcid":"https://orcid.org/0000-0003-4414-5722"},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Martin Lauer","raw_affiliation_strings":["Karlsruhe Institute of Technology (KIT), Institute of Measurement and Control Systems, Karlsruhe, Germany","Institute of Measurement and Control Systems, Karlsruhe Institute of Technology (KIT), Karlsruhe, 76131, Germany"],"affiliations":[{"raw_affiliation_string":"Karlsruhe Institute of Technology (KIT), Institute of Measurement and Control Systems, Karlsruhe, Germany","institution_ids":["https://openalex.org/I102335020"]},{"raw_affiliation_string":"Institute of Measurement and Control Systems, Karlsruhe Institute of Technology (KIT), Karlsruhe, 76131, Germany","institution_ids":["https://openalex.org/I102335020"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091574711","display_name":"Christoph Stiller","orcid":"https://orcid.org/0000-0003-4165-2075"},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Christoph Stiller","raw_affiliation_strings":["Karlsruhe Institute of Technology (KIT), Institute of Measurement and Control Systems, Karlsruhe, Germany","Institute of Measurement and Control Systems, Karlsruhe Institute of Technology (KIT), Karlsruhe, 76131, Germany"],"affiliations":[{"raw_affiliation_string":"Karlsruhe Institute of Technology (KIT), Institute of Measurement and Control Systems, Karlsruhe, Germany","institution_ids":["https://openalex.org/I102335020"]},{"raw_affiliation_string":"Institute of Measurement and Control Systems, Karlsruhe Institute of Technology (KIT), Karlsruhe, 76131, Germany","institution_ids":["https://openalex.org/I102335020"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5022113943"],"corresponding_institution_ids":["https://openalex.org/I891521709"],"apc_list":null,"apc_paid":null,"fwci":10.7658,"has_fulltext":false,"cited_by_count":115,"citation_normalized_percentile":{"value":0.98622965,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9998000264167786,"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":0.9998000264167786,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9954000115394592,"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/T10370","display_name":"Traffic and Road Safety","score":0.9912999868392944,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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":0.7645497918128967},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6553812026977539},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5929582715034485},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5709584951400757},{"id":"https://openalex.org/keywords/motion-planning","display_name":"Motion planning","score":0.5630818605422974},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.5616071820259094},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5167242884635925},{"id":"https://openalex.org/keywords/collision-avoidance","display_name":"Collision avoidance","score":0.513645350933075},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.4880586862564087},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.47127577662467957},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.42792800068855286},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4132019579410553},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32549524307250977},{"id":"https://openalex.org/keywords/collision","display_name":"Collision","score":0.29334557056427},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12066531181335449}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7645497918128967},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6553812026977539},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5929582715034485},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5709584951400757},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.5630818605422974},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.5616071820259094},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5167242884635925},{"id":"https://openalex.org/C2780864053","wikidata":"https://www.wikidata.org/wiki/Q5147495","display_name":"Collision avoidance","level":3,"score":0.513645350933075},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.4880586862564087},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.47127577662467957},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.42792800068855286},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4132019579410553},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32549524307250977},{"id":"https://openalex.org/C121704057","wikidata":"https://www.wikidata.org/wiki/Q352070","display_name":"Collision","level":2,"score":0.29334557056427},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12066531181335449},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icra.2018.8460203","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2018.8460203","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7699999809265137,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1579853615","https://openalex.org/W1903029394","https://openalex.org/W1980985548","https://openalex.org/W1999050017","https://openalex.org/W2001875386","https://openalex.org/W2004641798","https://openalex.org/W2004815690","https://openalex.org/W2097117768","https://openalex.org/W2101415982","https://openalex.org/W2101821104","https://openalex.org/W2245440101","https://openalex.org/W2286744228","https://openalex.org/W2293634267","https://openalex.org/W2340897893","https://openalex.org/W2415953079","https://openalex.org/W2911273949","https://openalex.org/W2963980401","https://openalex.org/W3021208093","https://openalex.org/W6674955169","https://openalex.org/W6692405165","https://openalex.org/W6696413620"],"related_works":["https://openalex.org/W2392100589","https://openalex.org/W2512789322","https://openalex.org/W2101960027","https://openalex.org/W2197846993","https://openalex.org/W1976827262","https://openalex.org/W49697837","https://openalex.org/W3122828758","https://openalex.org/W2170799233","https://openalex.org/W2768112316","https://openalex.org/W4381746183"],"abstract_inverted_index":{"Accurate":[0],"traffic":[1],"participant":[2],"prediction":[3,50],"is":[4],"the":[5,40,69,98],"prerequisite":[6],"for":[7,32],"collision":[8],"avoidance":[9],"of":[10,43,68],"autonomous":[11],"vehicles.":[12],"In":[13],"this":[14,74],"work,":[15],"we":[16,26],"propose":[17],"to":[18,101],"predict":[19,102],"pedestrians":[20],"using":[21],"goal-directed":[22],"planning.":[23],"For":[24],"this,":[25],"infer":[27],"a":[28,44,61],"mixture":[29],"density":[30],"function":[31],"possible":[33],"destinations.":[34],"We":[35,71],"use":[36],"these":[37],"destinations":[38,104],"as":[39,80],"goal":[41],"states":[42],"planning":[45],"stage":[46],"that":[47,73],"performs":[48],"motion":[49],"based":[51],"on":[52,66,93],"common":[53],"behavior":[54],"patterns.":[55],"The":[56],"patterns":[57],"are":[58],"learned":[59],"by":[60],"fully":[62],"convolutional":[63],"network":[64,84],"operating":[65],"maps":[67],"environment.":[70],"show":[72],"entire":[75],"system":[76],"can":[77],"be":[78],"modeled":[79],"one":[81],"monolithic":[82],"neural":[83],"and":[85,105],"trained":[86],"via":[87],"inverse":[88],"reinforcement":[89],"learning.":[90],"Experimental":[91],"validation":[92],"real":[94],"world":[95],"data":[96],"shows":[97],"system's":[99],"ability":[100],"both,":[103],"trajectories":[106],"accurately.":[107]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":13},{"year":2023,"cited_by_count":16},{"year":2022,"cited_by_count":12},{"year":2021,"cited_by_count":27},{"year":2020,"cited_by_count":20},{"year":2019,"cited_by_count":16},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":2}],"updated_date":"2026-02-16T05:59:50.152749","created_date":"2025-10-10T00:00:00"}
