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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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10971419/ |
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| _version_ | 1850141392025681920 |
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| author | Youngseong Cho Kyoungil Lim |
| author_facet | Youngseong Cho Kyoungil Lim |
| author_sort | Youngseong Cho |
| collection | DOAJ |
| description | 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 mechanisms and bird’s-eye view images. However, these approaches are often hindered by issues related to computational efficiency and the high costs of data acquisition for real-world applications. In contrast, the proposed method enhances the performance by integrating polynomial and differential networks, facilitating efficient learning while accounting for the physical properties inherent in the data. The results of experiments conducted using the CARLA simulator demonstrate that the proposed model outperforms existing state-of-the-art approaches. The model weights and training code used in these experiments are available at <uri>https://github.com/choys0401/polydiff</uri>. |
| format | Article |
| id | doaj-art-7554c036b2d54ecba61a25ef6bbb60f6 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-7554c036b2d54ecba61a25ef6bbb60f62025-08-20T02:29:27ZengIEEEIEEE Access2169-35362025-01-0113694926949910.1109/ACCESS.2025.356266610971419Polynomial and Differential Networks for End-to-End Autonomous DrivingYoungseong Cho0https://orcid.org/0009-0004-9460-8849Kyoungil Lim1https://orcid.org/0000-0003-4829-2383Advanced Institutes of Convergence Technology, Suwon, Republic of KoreaAdvanced Institutes of Convergence Technology, Suwon, Republic of KoreaThis 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 mechanisms and bird’s-eye view images. However, these approaches are often hindered by issues related to computational efficiency and the high costs of data acquisition for real-world applications. In contrast, the proposed method enhances the performance by integrating polynomial and differential networks, facilitating efficient learning while accounting for the physical properties inherent in the data. The results of experiments conducted using the CARLA simulator demonstrate that the proposed model outperforms existing state-of-the-art approaches. The model weights and training code used in these experiments are available at <uri>https://github.com/choys0401/polydiff</uri>.https://ieeexplore.ieee.org/document/10971419/Autonomous drivingdeep learningend-to-end drivingimitation learning |
| spellingShingle | Youngseong Cho Kyoungil Lim Polynomial and Differential Networks for End-to-End Autonomous Driving IEEE Access Autonomous driving deep learning end-to-end driving imitation learning |
| title | Polynomial and Differential Networks for End-to-End Autonomous Driving |
| title_full | Polynomial and Differential Networks for End-to-End Autonomous Driving |
| title_fullStr | Polynomial and Differential Networks for End-to-End Autonomous Driving |
| title_full_unstemmed | Polynomial and Differential Networks for End-to-End Autonomous Driving |
| title_short | Polynomial and Differential Networks for End-to-End Autonomous Driving |
| title_sort | polynomial and differential networks for end to end autonomous driving |
| topic | Autonomous driving deep learning end-to-end driving imitation learning |
| url | https://ieeexplore.ieee.org/document/10971419/ |
| work_keys_str_mv | AT youngseongcho polynomialanddifferentialnetworksforendtoendautonomousdriving AT kyoungillim polynomialanddifferentialnetworksforendtoendautonomousdriving |