As part of a complete software stack for autonomous driving, NVIDIA has
created a neural-network-based system, known as PilotNet, which outputs
steering angles given images of the road ahead. PilotNet is trained using road
images paired with the steering angles generated by a human driving a
data-collection car. It derives the necessary domain knowledge by observing
human drivers. This eliminates the need for human engineers to anticipate what
is important in an image and foresee all the necessary rules for safe driving.
Road tests demonstrated that PilotNet can successfully perform lane keeping in
a wide variety of driving conditions, regardless of whether lane markings are
present or not.
The goal of the work described here is to explain what PilotNet learns and
how it makes its decisions. To this end we developed a method for determining
which elements in the road image most influence PilotNet's steering decision.
Results show that PilotNet indeed learns to recognize relevant objects on the
road.
In addition to learning the obvious features such as lane markings, edges of
roads, and other cars, PilotNet learns more subtle features that would be hard
to anticipate and program by engineers, for example, bushes lining the edge of
the road and atypical vehicle classes.

NVIDIA 的整个自动驾驶软件中的神经网络系统 PilotNet 能够根据道路前方的图像输出转向角，其根据观测到的人类驾驶员的行为进行训练。本文旨在解释 PilotNet 学习的内容以及其如何作出决策，通过实现确定道路图像中最影响 PilotNet 转向决策的元素的方法，展示 PilotNet 不仅学习了显而易见的车道标记、路边的边缘和其他车辆等特性，还学会了更加微妙难以预料和规划的特征，例如路边的灌木丛和非典型车辆类别。