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Road To Reliability: Optimizing Self-Driving Consistency With Real-Time Speed Data

DescriptionSelf-driving cars can potentially improve transportation efficiency and reduce human fatalities – provided they have access to significant processing power and large amounts of data. One popular approach for actualizing autonomous vehicles is using end-to-end learning, in which a machine learning model is trained on a large data set of real human driving. This poster shows how self-driving consistency can be improved using a Convolutional Neural Network (CNN) to predict current velocity. Our approach first reproduces an end-to-end learning result and then extends it with real-time speed data as additional model input.

Author

William Fowler

Tufts University

Event Type

ACM Student Research Competition: Graduate Poster

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