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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.
Event Type
ACM Student Research Competition: Graduate Poster
ACM Student Research Competition: Undergraduate Poster
Posters
TimeWednesday, 15 November 202310am - 5pm MST
Tags
Artificial Intelligence/Machine Learning
Algorithms
Applications
Architecture and Networks
Cloud Computing
Distributed Computing
Data Analysis, Visualization, and Storage
Performance Measurement, Modeling, and Tools
Programming Frameworks and System Software
Registration Categories
TP
XO/EX