INTERSECTION DETECTION AND CLASSIFICATION IN AUTONOMOUS MACHINE APPLICATIONS
In various examples, live perception from sensors of a vehicle may be leveraged to detect and classify intersections in an environment of a vehicle in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute various outputs-such as bounding box coordinates for...
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 | Cvijetic, Neda Sajjadi Mohammadabadi, Sayed Mehdi Park, Minwoo Hervas, Berta Rodriguez Pham, Trung Kwon, Junghyun Dou, Hang Nister, David Tryndin, Igor |
description | In various examples, live perception from sensors of a vehicle may be leveraged to detect and classify intersections in an environment of a vehicle in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute various outputs-such as bounding box coordinates for intersections, intersection coverage maps corresponding to the bounding boxes, intersection attributes, distances to intersections, and/or distance coverage maps associated with the intersections. The outputs may be decoded and/or post-processed to determine final locations of, distances to, and/or attributes of the detected intersections. |
format | Patent |
fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_US2024101118A1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>US2024101118A1</sourcerecordid><originalsourceid>FETCH-epo_espacenet_US2024101118A13</originalsourceid><addsrcrecordid>eNrjZPDx9AtxDQp2dQ7x9PdTcHENgbIc_VwUnH0cg4M93TydHcFCnkDR0BB_P39f_9BgBV9HZw9PP1cFx4AAH6iKYB4G1rTEnOJUXijNzaDs5hri7KGbWpAfn1pckJicmpdaEh8abGRgZGJoYGhoaOFoaEycKgCkQi8x</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>INTERSECTION DETECTION AND CLASSIFICATION IN AUTONOMOUS MACHINE APPLICATIONS</title><source>esp@cenet</source><creator>Cvijetic, Neda ; Sajjadi Mohammadabadi, Sayed Mehdi ; Park, Minwoo ; Hervas, Berta Rodriguez ; Pham, Trung ; Kwon, Junghyun ; Dou, Hang ; Nister, David ; Tryndin, Igor</creator><creatorcontrib>Cvijetic, Neda ; Sajjadi Mohammadabadi, Sayed Mehdi ; Park, Minwoo ; Hervas, Berta Rodriguez ; Pham, Trung ; Kwon, Junghyun ; Dou, Hang ; Nister, David ; Tryndin, Igor</creatorcontrib><description>In various examples, live perception from sensors of a vehicle may be leveraged to detect and classify intersections in an environment of a vehicle in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute various outputs-such as bounding box coordinates for intersections, intersection coverage maps corresponding to the bounding boxes, intersection attributes, distances to intersections, and/or distance coverage maps associated with the intersections. The outputs may be decoded and/or post-processed to determine final locations of, distances to, and/or attributes of the detected intersections.</description><language>eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE ORDIFFERENT FUNCTION ; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES ; COUNTING ; PERFORMING OPERATIONS ; PHYSICS ; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TOTHE CONTROL OF A PARTICULAR SUB-UNIT ; SIGNALLING ; TRAFFIC CONTROL SYSTEMS ; TRANSPORTING ; VEHICLES IN GENERAL</subject><creationdate>2024</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=20240328&DB=EPODOC&CC=US&NR=2024101118A1$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20240328&DB=EPODOC&CC=US&NR=2024101118A1$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Cvijetic, Neda</creatorcontrib><creatorcontrib>Sajjadi Mohammadabadi, Sayed Mehdi</creatorcontrib><creatorcontrib>Park, Minwoo</creatorcontrib><creatorcontrib>Hervas, Berta Rodriguez</creatorcontrib><creatorcontrib>Pham, Trung</creatorcontrib><creatorcontrib>Kwon, Junghyun</creatorcontrib><creatorcontrib>Dou, Hang</creatorcontrib><creatorcontrib>Nister, David</creatorcontrib><creatorcontrib>Tryndin, Igor</creatorcontrib><title>INTERSECTION DETECTION AND CLASSIFICATION IN AUTONOMOUS MACHINE APPLICATIONS</title><description>In various examples, live perception from sensors of a vehicle may be leveraged to detect and classify intersections in an environment of a vehicle in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute various outputs-such as bounding box coordinates for intersections, intersection coverage maps corresponding to the bounding boxes, intersection attributes, distances to intersections, and/or distance coverage maps associated with the intersections. The outputs may be decoded and/or post-processed to determine final locations of, distances to, and/or attributes of the detected intersections.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE ORDIFFERENT FUNCTION</subject><subject>CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES</subject><subject>COUNTING</subject><subject>PERFORMING OPERATIONS</subject><subject>PHYSICS</subject><subject>ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TOTHE CONTROL OF A PARTICULAR SUB-UNIT</subject><subject>SIGNALLING</subject><subject>TRAFFIC CONTROL SYSTEMS</subject><subject>TRANSPORTING</subject><subject>VEHICLES IN GENERAL</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZPDx9AtxDQp2dQ7x9PdTcHENgbIc_VwUnH0cg4M93TydHcFCnkDR0BB_P39f_9BgBV9HZw9PP1cFx4AAH6iKYB4G1rTEnOJUXijNzaDs5hri7KGbWpAfn1pckJicmpdaEh8abGRgZGJoYGhoaOFoaEycKgCkQi8x</recordid><startdate>20240328</startdate><enddate>20240328</enddate><creator>Cvijetic, Neda</creator><creator>Sajjadi Mohammadabadi, Sayed Mehdi</creator><creator>Park, Minwoo</creator><creator>Hervas, Berta Rodriguez</creator><creator>Pham, Trung</creator><creator>Kwon, Junghyun</creator><creator>Dou, Hang</creator><creator>Nister, David</creator><creator>Tryndin, Igor</creator><scope>EVB</scope></search><sort><creationdate>20240328</creationdate><title>INTERSECTION DETECTION AND CLASSIFICATION IN AUTONOMOUS MACHINE APPLICATIONS</title><author>Cvijetic, Neda ; Sajjadi Mohammadabadi, Sayed Mehdi ; Park, Minwoo ; Hervas, Berta Rodriguez ; Pham, Trung ; Kwon, Junghyun ; Dou, Hang ; Nister, David ; Tryndin, Igor</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US2024101118A13</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2024</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE ORDIFFERENT FUNCTION</topic><topic>CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES</topic><topic>COUNTING</topic><topic>PERFORMING OPERATIONS</topic><topic>PHYSICS</topic><topic>ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TOTHE CONTROL OF A PARTICULAR SUB-UNIT</topic><topic>SIGNALLING</topic><topic>TRAFFIC CONTROL SYSTEMS</topic><topic>TRANSPORTING</topic><topic>VEHICLES IN GENERAL</topic><toplevel>online_resources</toplevel><creatorcontrib>Cvijetic, Neda</creatorcontrib><creatorcontrib>Sajjadi Mohammadabadi, Sayed Mehdi</creatorcontrib><creatorcontrib>Park, Minwoo</creatorcontrib><creatorcontrib>Hervas, Berta Rodriguez</creatorcontrib><creatorcontrib>Pham, Trung</creatorcontrib><creatorcontrib>Kwon, Junghyun</creatorcontrib><creatorcontrib>Dou, Hang</creatorcontrib><creatorcontrib>Nister, David</creatorcontrib><creatorcontrib>Tryndin, Igor</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Cvijetic, Neda</au><au>Sajjadi Mohammadabadi, Sayed Mehdi</au><au>Park, Minwoo</au><au>Hervas, Berta Rodriguez</au><au>Pham, Trung</au><au>Kwon, Junghyun</au><au>Dou, Hang</au><au>Nister, David</au><au>Tryndin, Igor</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>INTERSECTION DETECTION AND CLASSIFICATION IN AUTONOMOUS MACHINE APPLICATIONS</title><date>2024-03-28</date><risdate>2024</risdate><abstract>In various examples, live perception from sensors of a vehicle may be leveraged to detect and classify intersections in an environment of a vehicle in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute various outputs-such as bounding box coordinates for intersections, intersection coverage maps corresponding to the bounding boxes, intersection attributes, distances to intersections, and/or distance coverage maps associated with the intersections. The outputs may be decoded and/or post-processed to determine final locations of, distances to, and/or attributes of the detected intersections.</abstract><oa>free_for_read</oa></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | |
ispartof | |
issn | |
language | eng |
recordid | cdi_epo_espacenet_US2024101118A1 |
source | esp@cenet |
subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE ORDIFFERENT FUNCTION CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES COUNTING PERFORMING OPERATIONS PHYSICS ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TOTHE CONTROL OF A PARTICULAR SUB-UNIT SIGNALLING TRAFFIC CONTROL SYSTEMS TRANSPORTING VEHICLES IN GENERAL |
title | INTERSECTION DETECTION AND CLASSIFICATION IN AUTONOMOUS MACHINE APPLICATIONS |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T02%3A19%3A16IST&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=Cvijetic,%20Neda&rft.date=2024-03-28&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3EUS2024101118A1%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 |