  This paper presents a safe imitation learning approach for autonomous vehicle
driving, with attention on real-life human driving data and experimental
validation. In order to increase occupant's acceptance and gain drivers' trust,
the autonomous driving function needs to provide a both safe and comfortable
behavior such as risk-free and naturalistic driving. Our goal is to obtain such
behavior via imitation learning of a planning policy from human driving data.
In particular, we propose to incorporate barrier functions and smooth
spline-based motion parametrization in the training loss function. The
advantage is twofold: improving safety of the learning algorithm, while
reducing the amount of needed training data. Moreover, the behavior is learned
from highway driving data, which is collected consistently by a human driver
and then processed towards a specific driving scenario. For development
validation, a digital twin of the real test vehicle, sensors, and traffic
scenarios are reconstructed toward high-fidelity and physics-based modeling
technologies. These models are imported to simulation tools and co-simulated
with the proposed algorithm for validation and further testing. Finally, we
present experimental results and analyses, and compare with the conventional
imitation learning technique (behavioral cloning) to justify the proposed
development.
