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: | , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
IEEE
2025-01-01
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| coleção: | IEEE Access |
| Assuntos: | |
| Acesso em linha: | https://ieeexplore.ieee.org/document/10971419/ |
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