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.

本文快速回顾了当前在工业界和学术界报道的方法和技术，具体地评估了包括特斯拉 FSD V12、Momenta 2023、Horizon Robotics 2023、Motional RoboTaxi 2022、Woven Planet（丰田）：城市司机和 Nvidia 在内的端到端规划。此外，我们还回顾了研究自主驾驶端到端规划的学术研究领域的最新研究。本文为读者提供了有关 2022-2023 年最新端到端规划的简洁结构和快速学习方法。本文既是初学者了解工业界和学术界自主驾驶端到端规划最新进展的入门材料，也是高级研究人员的补充资料。

行业和学界的自动驾驶端到端规划：2022-2023

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

Thanks to the augmented convenience, safety advantages, and potential
commercial value, Intelligent vehicles (IVs) have attracted wide attention
throughout the world. Although a few of autonomous driving unicorns assert that
IVs will be commercially deployable by 2025, their implementation is still
restricted to small-scale validation due to various issues, among which precise
computation of control commands or trajectories by planning methods remains a
prerequisite for IVs. This paper aims to review state-of-the-art planning
methods, including pipeline planning and end-to-end planning methods. In terms
of pipeline methods, a survey of selecting algorithms is provided along with a
discussion of the expansion and optimization mechanisms, whereas in end-to-end
methods, the training approaches and verification scenarios of driving tasks
are points of concern. Experimental platforms are reviewed to facilitate
readers in selecting suitable training and validation methods. Finally, the
current challenges and future directions are discussed. The side-by-side
comparison presented in this survey not only helps to gain insights into the
strengths and limitations of the reviewed methods but also assists with
system-level design choices.

该论文旨在综述智能车辆的规划方法，介绍了管道规划和端到端规划方法，并探讨了挑选算法和训练方法的不同考虑因素以及当前的挑战和未来方向。

自动驾驶的运动规划：现状和展望

Motion Planning for Autonomous Driving: The State of the Art and Perspectives

We study how group symmetry helps improve data efficiency and generalization
for end-to-end differentiable planning algorithms when symmetry appears in
decision-making tasks. Motivated by equivariant convolution networks, we treat
the path planning problem as \textit{signals} over grids. We show that value
iteration in this case is a linear equivariant operator, which is a (steerable)
convolution. This extends Value Iteration Networks (VINs) on using
convolutional networks for path planning with additional rotation and
reflection symmetry. Our implementation is based on VINs and uses steerable
convolution networks to incorporate symmetry. The experiments are performed on
four tasks: 2D navigation, visual navigation, and 2 degrees of freedom (2DOFs)
configuration space and workspace manipulation. Our symmetric planning
algorithms improve training efficiency and generalization by large margins
compared to non-equivariant counterparts, VIN and GPPN.

该论文研究了如何使用群对称性改善端到端可微规划算法的数据效率和泛化能力，当对称性出现在决策任务中时。 通过等变卷积网络的启发，将路径规划问题视为网格上的信号，并表明在此情况下，价值迭代是一个线性的等变算子，即一个（可转向）卷积。 基于 VIN 的实现，使用等变卷积网络来纳入对称性。 在三个路径规划任务和一个工作空间操纵任务中的实验表明，我们的算法较 VIN 和 GPPN 的非等变协方差算法提高了大量的训练效率和泛化能力。