论文标题
NVIDIA PILOTNET实验
The NVIDIA PilotNet Experiments
论文作者
论文摘要
四年前，一个被称为Pilotnet的实验系统成为第一个沿着道路引导自动驾驶汽车的NVIDIA系统。该系统代表着自动驾驶的经典方法，其中该过程被手动分解为一系列模块，每个模块执行了不同的任务。另一方面，在Pilotnet中，单个深神经网络（DNN）将像素作为输入并产生所需的车辆轨迹作为输出。没有由人设计的界面连接的独特的内部模块。我们认为，手工制作的接口最终通过限制通过系统的信息流来限制性能，并且一种与其他增加冗余的人工智能系统结合使用的方法将导致更好的整体性能系统。我们继续针对该目标进行研究。 该文档描述了我们在新泽西州霍尔姆德尔的NVIDIA PILOTNET集团在过去五年中进行的Pilotnet Lane保存工作。在这里，我们介绍了2020年中期的系统状态快照，并突出了Pilotnet Group所做的一些工作。
Four years ago, an experimental system known as PilotNet became the first NVIDIA system to steer an autonomous car along a roadway. This system represents a departure from the classical approach for self-driving in which the process is manually decomposed into a series of modules, each performing a different task. In PilotNet, on the other hand, a single deep neural network (DNN) takes pixels as input and produces a desired vehicle trajectory as output; there are no distinct internal modules connected by human-designed interfaces. We believe that handcrafted interfaces ultimately limit performance by restricting information flow through the system and that a learned approach, in combination with other artificial intelligence systems that add redundancy, will lead to better overall performing systems. We continue to conduct research toward that goal. This document describes the PilotNet lane-keeping effort, carried out over the past five years by our NVIDIA PilotNet group in Holmdel, New Jersey. Here we present a snapshot of system status in mid-2020 and highlight some of the work done by the PilotNet group.
