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.
翻译:在自驾业中,机器学习的使用促进了最近的一些进展,特别是,在感知和预测堆中使用大型深层学习模型证明相当成功,但在规划堆中仍缺乏关于机器学习使用的大量文献。规划堆中目前的先进状态往往依赖于快速限制优化或基于规则的方法。这两种技术都未能解决大量根本问题,使车辆的运作与人类驾驶员的操作更为相似。在本文件中,我们试图设计一个基本的深层学习系统来处理这一问题。此外,本文的主要基本目标是展示机器学习在自主车辆规划堆中的潜在用途,并为当前和今后的研究提供基线工作。