论文标题
设计一个经常性的神经网络，以学习高维输入的运动计划者
Designing a Recurrent Neural Network to Learn a Motion Planner for High-Dimensional Inputs
论文作者
论文摘要
在自动驾驶行业中使用机器学习已提高了许多最近的进步。特别是，在感知和预测堆栈中，大型深度学习模型的使用已被证明很成功，但仍然缺乏有关在计划堆栈中使用机器学习的重要文献。计划堆栈中的当前艺术状态通常依赖于快速约束的优化或基于规则的方法。这两种技术都无法解决许多基本问题，这些问题将使车辆与人类驾驶员的操作更相似。在本文中，我们试图设计一个基本的深度学习系统来解决此问题。此外，本文的主要基础目标是证明机器学习在自动驾驶汽车（AV）（AV）中的潜在用途，并为正在进行和未来的研究提供了基线工作。
The use of machine learning in the self-driving industry has boosted a number of recent advancements. In particular, the usage of large deep learning models in the perception and prediction stack have proved quite successful, but there still lacks significant literature on the use of machine learning in the planning stack. The current state of the art in the planning stack often relies on fast constrained optimization or rule-based approaches. Both of these techniques fail to address a significant number of fundamental problems that would allow the vehicle to operate more similarly to that of human drivers. In this paper, we attempt to design a basic deep learning system to approach this problem. Furthermore, the main underlying goal of this paper is to demonstrate the potential uses of machine learning in the planning stack for autonomous vehicles (AV) and provide a baseline work for ongoing and future research.
