Trajectory Generation Using Road Network Model
Enclosed are embodiments for trajectory generation using a road network model. In an embodiment, a method includes: obtaining, using at least one processor of a vehicle, a location of the vehicle; obtaining, using the at least one processor, sensor data collected at the location; obtaining, using th...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Patent |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | |
| container_volume | |
| creator | Wolff, Eric Beaudoin, Robert |
| description | Enclosed are embodiments for trajectory generation using a road network model. In an embodiment, a method includes: obtaining, using at least one processor of a vehicle, a location of the vehicle; obtaining, using the at least one processor, sensor data collected at the location; obtaining, using the at least one processor, map data for the location; generating, using the one or more processors, at least on possible trajectory for at least one object at the location, wherein the possible trajectory is constrained in accordance with the map data; and predicting, using a machine learning model, a score for the at least one trajectory. |
| format | Patent |
| fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_US2022101155A1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>US2022101155A1</sourcerecordid><originalsourceid>FETCH-epo_espacenet_US2022101155A13</originalsourceid><addsrcrecordid>eNrjZNALKUrMSk0uyS-qVHBPzUstSizJzM9TCC3OzEtXCMpPTFHwSy0pzy_KVvDNT0nN4WFgTUvMKU7lhdLcDMpuriHOHrqpBfnxqcUFiclAM0riQ4ONDIyMDA0MDU1NHQ2NiVMFAI9YKyk</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Trajectory Generation Using Road Network Model</title><source>esp@cenet</source><creator>Wolff, Eric ; Beaudoin, Robert</creator><creatorcontrib>Wolff, Eric ; Beaudoin, Robert</creatorcontrib><description>Enclosed are embodiments for trajectory generation using a road network model. In an embodiment, a method includes: obtaining, using at least one processor of a vehicle, a location of the vehicle; obtaining, using the at least one processor, sensor data collected at the location; obtaining, using the at least one processor, map data for the location; generating, using the one or more processors, at least on possible trajectory for at least one object at the location, wherein the possible trajectory is constrained in accordance with the map data; and predicting, using a machine learning model, a score for the at least one trajectory.</description><language>eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; PHYSICS ; SIGNALLING ; TRAFFIC CONTROL SYSTEMS</subject><creationdate>2022</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20220331&DB=EPODOC&CC=US&NR=2022101155A1$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20220331&DB=EPODOC&CC=US&NR=2022101155A1$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Wolff, Eric</creatorcontrib><creatorcontrib>Beaudoin, Robert</creatorcontrib><title>Trajectory Generation Using Road Network Model</title><description>Enclosed are embodiments for trajectory generation using a road network model. In an embodiment, a method includes: obtaining, using at least one processor of a vehicle, a location of the vehicle; obtaining, using the at least one processor, sensor data collected at the location; obtaining, using the at least one processor, map data for the location; generating, using the one or more processors, at least on possible trajectory for at least one object at the location, wherein the possible trajectory is constrained in accordance with the map data; and predicting, using a machine learning model, a score for the at least one trajectory.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>PHYSICS</subject><subject>SIGNALLING</subject><subject>TRAFFIC CONTROL SYSTEMS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2022</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZNALKUrMSk0uyS-qVHBPzUstSizJzM9TCC3OzEtXCMpPTFHwSy0pzy_KVvDNT0nN4WFgTUvMKU7lhdLcDMpuriHOHrqpBfnxqcUFiclAM0riQ4ONDIyMDA0MDU1NHQ2NiVMFAI9YKyk</recordid><startdate>20220331</startdate><enddate>20220331</enddate><creator>Wolff, Eric</creator><creator>Beaudoin, Robert</creator><scope>EVB</scope></search><sort><creationdate>20220331</creationdate><title>Trajectory Generation Using Road Network Model</title><author>Wolff, Eric ; Beaudoin, Robert</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US2022101155A13</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2022</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>PHYSICS</topic><topic>SIGNALLING</topic><topic>TRAFFIC CONTROL SYSTEMS</topic><toplevel>online_resources</toplevel><creatorcontrib>Wolff, Eric</creatorcontrib><creatorcontrib>Beaudoin, Robert</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wolff, Eric</au><au>Beaudoin, Robert</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Trajectory Generation Using Road Network Model</title><date>2022-03-31</date><risdate>2022</risdate><abstract>Enclosed are embodiments for trajectory generation using a road network model. In an embodiment, a method includes: obtaining, using at least one processor of a vehicle, a location of the vehicle; obtaining, using the at least one processor, sensor data collected at the location; obtaining, using the at least one processor, map data for the location; generating, using the one or more processors, at least on possible trajectory for at least one object at the location, wherein the possible trajectory is constrained in accordance with the map data; and predicting, using a machine learning model, a score for the at least one trajectory.</abstract><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | |
| ispartof | |
| issn | |
| language | eng |
| recordid | cdi_epo_espacenet_US2022101155A1 |
| source | esp@cenet |
| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING PHYSICS SIGNALLING TRAFFIC CONTROL SYSTEMS |
| title | Trajectory Generation Using Road Network Model |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-05T00%3A53%3A44IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=Wolff,%20Eric&rft.date=2022-03-31&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3EUS2022101155A1%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |