Refining event triggers using machine learning model feedback
A vehicle device may execute one or more neural networks (and/or other artificial intelligence), such as based on input from one or more of the cameras and/or other sensors associated with the dash cam, to intelligently detect safety events in real-time. The vehicle device may further pass the input...
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 | Tuan, Brian Akhtar, Muhammad Ali Kellerman, Bruce Srinivasan, Sharan Shieh, Vincent Bicket, John Wang, Jing Acevedo, Abner Ayala |
description | A vehicle device may execute one or more neural networks (and/or other artificial intelligence), such as based on input from one or more of the cameras and/or other sensors associated with the dash cam, to intelligently detect safety events in real-time. The vehicle device may further pass the input to a backend server for further analysis and the backend server can detect safety events based on the input. The vehicle device may analyze the output of the vehicle device and the output of the backend server to determine whether the output of the vehicle device is correct. If the output of the vehicle device is incorrect, the vehicle device can adjust how the vehicle device identifies safety events. |
format | Patent |
fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_US11352013B1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>US11352013B1</sourcerecordid><originalsourceid>FETCH-epo_espacenet_US11352013B13</originalsourceid><addsrcrecordid>eNrjZLANSk3LzMvMS1dILUvNK1EoKcpMT08tKlYoLQYJ5iYmZ2TmpSrkpCYWgVXl5qek5iikpaamJCUmZ_MwsKYl5hSn8kJpbgZFN9cQZw_d1IL8-NTigsTk1LzUkvjQYENDY1MjA0NjJ0NjYtQAADHtMFI</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Refining event triggers using machine learning model feedback</title><source>esp@cenet</source><creator>Tuan, Brian ; Akhtar, Muhammad Ali ; Kellerman, Bruce ; Srinivasan, Sharan ; Shieh, Vincent ; Bicket, John ; Wang, Jing ; Acevedo, Abner Ayala</creator><creatorcontrib>Tuan, Brian ; Akhtar, Muhammad Ali ; Kellerman, Bruce ; Srinivasan, Sharan ; Shieh, Vincent ; Bicket, John ; Wang, Jing ; Acevedo, Abner Ayala</creatorcontrib><description>A vehicle device may execute one or more neural networks (and/or other artificial intelligence), such as based on input from one or more of the cameras and/or other sensors associated with the dash cam, to intelligently detect safety events in real-time. The vehicle device may further pass the input to a backend server for further analysis and the backend server can detect safety events based on the input. The vehicle device may analyze the output of the vehicle device and the output of the backend server to determine whether the output of the vehicle device is correct. If the output of the vehicle device is incorrect, the vehicle device can adjust how the vehicle device identifies safety events.</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 ; IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ; PERFORMING OPERATIONS ; PHYSICS ; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TOTHE CONTROL OF A PARTICULAR SUB-UNIT ; TRANSPORTING ; VEHICLES IN GENERAL</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=20220607&DB=EPODOC&CC=US&NR=11352013B1$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,780,885,25564,76547</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20220607&DB=EPODOC&CC=US&NR=11352013B1$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>Tuan, Brian</creatorcontrib><creatorcontrib>Akhtar, Muhammad Ali</creatorcontrib><creatorcontrib>Kellerman, Bruce</creatorcontrib><creatorcontrib>Srinivasan, Sharan</creatorcontrib><creatorcontrib>Shieh, Vincent</creatorcontrib><creatorcontrib>Bicket, John</creatorcontrib><creatorcontrib>Wang, Jing</creatorcontrib><creatorcontrib>Acevedo, Abner Ayala</creatorcontrib><title>Refining event triggers using machine learning model feedback</title><description>A vehicle device may execute one or more neural networks (and/or other artificial intelligence), such as based on input from one or more of the cameras and/or other sensors associated with the dash cam, to intelligently detect safety events in real-time. The vehicle device may further pass the input to a backend server for further analysis and the backend server can detect safety events based on the input. The vehicle device may analyze the output of the vehicle device and the output of the backend server to determine whether the output of the vehicle device is correct. If the output of the vehicle device is incorrect, the vehicle device can adjust how the vehicle device identifies safety events.</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>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</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>TRANSPORTING</subject><subject>VEHICLES IN GENERAL</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2022</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZLANSk3LzMvMS1dILUvNK1EoKcpMT08tKlYoLQYJ5iYmZ2TmpSrkpCYWgVXl5qek5iikpaamJCUmZ_MwsKYl5hSn8kJpbgZFN9cQZw_d1IL8-NTigsTk1LzUkvjQYENDY1MjA0NjJ0NjYtQAADHtMFI</recordid><startdate>20220607</startdate><enddate>20220607</enddate><creator>Tuan, Brian</creator><creator>Akhtar, Muhammad Ali</creator><creator>Kellerman, Bruce</creator><creator>Srinivasan, Sharan</creator><creator>Shieh, Vincent</creator><creator>Bicket, John</creator><creator>Wang, Jing</creator><creator>Acevedo, Abner Ayala</creator><scope>EVB</scope></search><sort><creationdate>20220607</creationdate><title>Refining event triggers using machine learning model feedback</title><author>Tuan, Brian ; Akhtar, Muhammad Ali ; Kellerman, Bruce ; Srinivasan, Sharan ; Shieh, Vincent ; Bicket, John ; Wang, Jing ; Acevedo, Abner Ayala</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US11352013B13</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>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>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</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>TRANSPORTING</topic><topic>VEHICLES IN GENERAL</topic><toplevel>online_resources</toplevel><creatorcontrib>Tuan, Brian</creatorcontrib><creatorcontrib>Akhtar, Muhammad Ali</creatorcontrib><creatorcontrib>Kellerman, Bruce</creatorcontrib><creatorcontrib>Srinivasan, Sharan</creatorcontrib><creatorcontrib>Shieh, Vincent</creatorcontrib><creatorcontrib>Bicket, John</creatorcontrib><creatorcontrib>Wang, Jing</creatorcontrib><creatorcontrib>Acevedo, Abner Ayala</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Tuan, Brian</au><au>Akhtar, Muhammad Ali</au><au>Kellerman, Bruce</au><au>Srinivasan, Sharan</au><au>Shieh, Vincent</au><au>Bicket, John</au><au>Wang, Jing</au><au>Acevedo, Abner Ayala</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Refining event triggers using machine learning model feedback</title><date>2022-06-07</date><risdate>2022</risdate><abstract>A vehicle device may execute one or more neural networks (and/or other artificial intelligence), such as based on input from one or more of the cameras and/or other sensors associated with the dash cam, to intelligently detect safety events in real-time. The vehicle device may further pass the input to a backend server for further analysis and the backend server can detect safety events based on the input. The vehicle device may analyze the output of the vehicle device and the output of the backend server to determine whether the output of the vehicle device is correct. If the output of the vehicle device is incorrect, the vehicle device can adjust how the vehicle device identifies safety events.</abstract><oa>free_for_read</oa></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | |
ispartof | |
issn | |
language | eng |
recordid | cdi_epo_espacenet_US11352013B1 |
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 IMAGE DATA PROCESSING OR GENERATION, IN GENERAL PERFORMING OPERATIONS PHYSICS ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TOTHE CONTROL OF A PARTICULAR SUB-UNIT TRANSPORTING VEHICLES IN GENERAL |
title | Refining event triggers using machine learning model feedback |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-29T03%3A32%3A32IST&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=Tuan,%20Brian&rft.date=2022-06-07&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3EUS11352013B1%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 |