Explaining Autonomous Vehicles with Intention-aware Policy Graphs

TL;DR

This paper introduces a post-hoc, model-agnostic method using intention-aware policy graphs to generate interpretable explanations of autonomous vehicle behavior, enhancing trust and safety assessment in urban environments.

Contribution

It presents a novel approach for explaining autonomous vehicle decisions post-hoc, improving interpretability without altering existing AI models.

Findings

Effective extraction of explanations from nuScenes dataset

Ability to assess legal compliance of vehicle behavior

Identification of vulnerabilities in datasets and models

Abstract

The potential to improve road safety, reduce human driving error, and promote environmental sustainability have enabled the field of autonomous driving to progress rapidly over recent decades. The performance of autonomous vehicles has significantly improved thanks to advancements in Artificial Intelligence, particularly Deep Learning. Nevertheless, the opacity of their decision-making, rooted in the use of accurate yet complex AI models, has created barriers to their societal trust and regulatory acceptance, raising the need for explainability. We propose a post-hoc, model-agnostic solution to provide teleological explanations for the behaviour of an autonomous vehicle in urban environments. Building on Intention-aware Policy Graphs, our approach enables the extraction of interpretable and reliable explanations of vehicle behaviour in the nuScenes dataset from global and local perspectives. We demonstrate the potential of these explanations to assess whether the vehicle operates within acceptable legal boundaries and to identify possible vulnerabilities in autonomous driving datasets and models.