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Polynomial and Differential Networks for End-to-End Autonomous Driving

This study introduces a novel model for predicting control variables in end-to-end autonomous driving by leveraging polynomial and differential networks. Recent advancements in autonomous driving have predominantly focused on methods that incorporate additional supervisory data, such as attention me...

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Principais autores: Youngseong Cho, Kyoungil Lim
Format: Artigo
Sprog:Inglês
Udgivet: IEEE 2025-01-01
Serier:IEEE Access
Fag:
Autonomous driving
deep learning
end-to-end driving
imitation learning
Online adgang:https://ieeexplore.ieee.org/document/10971419/
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