Exploring Latent Pathways: Enhancing the Interpretability of Autonomous Driving with a Variational Autoencoder

TL;DR

This paper integrates a variational autoencoder with a neural circuit policy for autonomous driving, improving interpretability and providing tools to understand latent features influencing steering decisions.

Contribution

It introduces a novel combination of variational autoencoder with neural circuit policy and an automatic latent perturbation tool for enhanced interpretability.

Findings

Enhanced interpretability of autonomous driving models.

Automatic latent perturbation provides granular insights.

Demonstrated effectiveness through numerical experiments.

Abstract

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.