End-To-End Planning of Autonomous Driving in Industry and Academia: 2022-2023

• URL: http://arxiv.org/abs/2401.08658v1
• Date: Tue, 26 Dec 2023 12:00:58 GMT
• Title: End-To-End Planning of Autonomous Driving in Industry and Academia: 2022-2023
• Authors: Gongjin Lan an Qi Hao
• Abstract summary: 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. This paper provides readers with a concise structure and fast learning of state-of-the-art end-to-end planning for 2022-2023.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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