Symbolic Imitation Learning: From Black-Box to Explainable Driving
  Policies
Iman Sharifi Saber Fallah
Current methods of imitation learning (IL), primarily based on deep neural
networks, offer efficient means for obtaining driving policies from real-world
data but suffer from significant limitations in interpretability and
generalizability. These shortcomings are particularly concerning in
safety-critical applications like autonomous driving. In this paper, we address
these limitations by introducing Symbolic Imitation Learning (SIL), a
groundbreaking method that employs Inductive Logic Programming (ILP) to learn
driving policies which are transparent, explainable and generalisable from
available datasets. Utilizing the real-world highD dataset, we subject our
method to a rigorous comparative analysis against prevailing
neural-network-based IL methods. Our results demonstrate that SIL not only
enhances the interpretability of driving policies but also significantly
improves their applicability across varied driving situations. Hence, this work
offers a novel pathway to more reliable and safer autonomous driving systems,
underscoring the potential of integrating ILP into the domain of IL.