Exploring Latent Pathways: Enhancing the Interpretability of Autonomous
  Driving with a Variational Autoencoder
Anass Bairouk Mirjana Maras Simon Herlin Alexander Amini Marc Blanchon Ramin Hasani Patrick Chareyre Daniela Rus
Autonomous driving presents a complex challenge, which is usually addressed
with artificial intelligence models that are end-to-end or modular in nature.
Within the landscape of modular approaches, a bio-inspired neural circuit
policy model has emerged as an innovative control module, offering a compact
and inherently interpretable system to infer a steering wheel command from
abstract visual features. Here, we take a leap forward by integrating a
variational autoencoder with the neural circuit policy controller, forming a
solution that directly generates steering commands from input camera images. By
substituting the traditional convolutional neural network approach to feature
extraction with a variational autoencoder, we enhance the system's
interpretability, enabling a more transparent and understandable
decision-making process.
  In addition to the architectural shift toward a variational autoencoder, this
study introduces the automatic latent perturbation tool, a novel contribution
designed to probe and elucidate the latent features within the variational
autoencoder. The automatic latent perturbation tool automates the
interpretability process, offering granular insights into how specific latent
variables influence the overall model's behavior. Through a series of numerical
experiments, we demonstrate the interpretative power of the variational
autoencoder-neural circuit policy model and the utility of the automatic latent
perturbation tool in making the inner workings of autonomous driving systems
more transparent.