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DTSTART;TZID=America/Denver:20231116T100000
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UID:submissions.supercomputing.org_SC23_sess300_spostu107@linklings.com
SUMMARY:Road To Reliability:  Optimizing Self-Driving Consistency With Rea
 l-Time Speed Data
DESCRIPTION:William Fowler (Tufts University)\n\nSelf-driving cars can pot
 entially improve transportation efficiency and reduce human fatalities – p
 rovided they have access to significant processing power and large amounts
  of data. One popular approach for actualizing autonomous vehicles is usin
 g end-to-end learning, in which a machine learning model is trained on a l
 arge data set of real human driving. This poster shows how self-driving co
 nsistency can be improved using a Convolutional Neural Network (CNN) to pr
 edict current velocity. Our approach first reproduces an end-to-end learni
 ng result and then extends it with real-time speed data as additional mode
 l input.\n\nRegistration Category: Tech Program Reg Pass, Exhibits Reg Pas
 s\n\n
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