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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2401.08658 |
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| _version_ | 1866913808934502400 |
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| author | Lan, Gongjin Hao, Qi |
| author_facet | Lan, Gongjin Hao, Qi |
| contents | This paper aims to provide a quick review of the methods including the technologies in detail that are currently reported in industry and academia. Specifically, this paper reviews the end-to-end planning, including Tesla FSD V12, Momenta 2023, Horizon Robotics 2023, Motional RoboTaxi 2022, Woven Planet (Toyota): Urban Driver, and Nvidia. In addition, we review the state-of-the-art academic studies that investigate end-to-end planning of autonomous driving. This paper provides readers with a concise structure and fast learning of state-of-the-art end-to-end planning for 2022-2023. This article provides a meaningful overview as introductory material for beginners to follow the state-of-the-art end-to-end planning of autonomous driving in industry and academia, as well as supplementary material for advanced researchers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_08658 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | End-To-End Planning of Autonomous Driving in Industry and Academia: 2022-2023 Lan, Gongjin Hao, Qi Robotics Artificial Intelligence This paper aims to provide a quick review of the methods including the technologies in detail that are currently reported in industry and academia. Specifically, this paper reviews the end-to-end planning, including Tesla FSD V12, Momenta 2023, Horizon Robotics 2023, Motional RoboTaxi 2022, Woven Planet (Toyota): Urban Driver, and Nvidia. In addition, we review the state-of-the-art academic studies that investigate end-to-end planning of autonomous driving. This paper provides readers with a concise structure and fast learning of state-of-the-art end-to-end planning for 2022-2023. This article provides a meaningful overview as introductory material for beginners to follow the state-of-the-art end-to-end planning of autonomous driving in industry and academia, as well as supplementary material for advanced researchers. |
| title | End-To-End Planning of Autonomous Driving in Industry and Academia: 2022-2023 |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2401.08658 |