Adversarial learning of driving behavior
Embodiments described herein disclose methods and systems for adversarial learning in autonomous vehicle path modeling. The systems and methods collect states of the vehicle in the environment to predict a path. The predicted path is compared for variance from an actual path. The variance between th...
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| creator | Olabiyi, Oluwatobi O |
| description | Embodiments described herein disclose methods and systems for adversarial learning in autonomous vehicle path modeling. The systems and methods collect states of the vehicle in the environment to predict a path. The predicted path is compared for variance from an actual path. The variance between the paths, in light of other data, is used to modify the driving models, to create more accurate representations of expert driving in autonomous vehicle path generation. |
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The predicted path is compared for variance from an actual path. 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The variance between the paths, in light of other data, is used to modify the driving models, to create more accurate representations of expert driving in autonomous vehicle path generation.</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>CONTROLLING</subject><subject>COUNTING</subject><subject>PHYSICS</subject><subject>REGULATING</subject><subject>SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZNBwTClLLSpOLMpMzFHISU0sysvMS1fIT1NIKcosAzGTUjMSyzLzi3gYWNMSc4pTeaE0N4Oim2uIs4duakF-fGpxQWJyal5qSXxosKGhmbGZsbmpk5ExMWoA4ZIomQ</recordid><startdate>20230425</startdate><enddate>20230425</enddate><creator>Olabiyi, Oluwatobi O</creator><scope>EVB</scope></search><sort><creationdate>20230425</creationdate><title>Adversarial learning of driving behavior</title><author>Olabiyi, Oluwatobi O</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_US11636375B23</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng</language><creationdate>2023</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>CONTROLLING</topic><topic>COUNTING</topic><topic>PHYSICS</topic><topic>REGULATING</topic><topic>SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES</topic><toplevel>online_resources</toplevel><creatorcontrib>Olabiyi, Oluwatobi O</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Olabiyi, Oluwatobi O</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Adversarial learning of driving behavior</title><date>2023-04-25</date><risdate>2023</risdate><abstract>Embodiments described herein disclose methods and systems for adversarial learning in autonomous vehicle path modeling. The systems and methods collect states of the vehicle in the environment to predict a path. The predicted path is compared for variance from an actual path. The variance between the paths, in light of other data, is used to modify the driving models, to create more accurate representations of expert driving in autonomous vehicle path generation.</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING CONTROLLING COUNTING PHYSICS REGULATING SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES |
| title | Adversarial learning of driving behavior |
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