  In autonomous driving, the end-to-end (E2E) driving approach that predicts
vehicle control signals directly from sensor data is rapidly gaining attention.
To learn a safe E2E driving system, one needs an extensive amount of driving
data and human intervention. Vehicle control data is constructed by many hours
of human driving, and it is challenging to construct large vehicle control
datasets. Often, publicly available driving datasets are collected with limited
driving scenes, and collecting vehicle control data is only available by
vehicle manufacturers. To address these challenges, this paper proposes the
first self-supervised learning framework, self-supervised imitation learning
(SSIL), that can learn E2E driving networks without using driving command data.
To construct pseudo steering angle data, proposed SSIL predicts a pseudo target
from the vehicle's poses at the current and previous time points that are
estimated with light detection and ranging sensors. Our numerical experiments
demonstrate that the proposed SSIL framework achieves comparable E2E driving
accuracy with the supervised learning counterpart. In addition, our qualitative
analyses using a conventional visual explanation tool show that trained NNs by
proposed SSIL and the supervision counterpart attend similar objects in making
predictions.
