Towards Safe, Explainable, and Regulated Autonomous Driving

TL;DR

This paper proposes a comprehensive framework integrating autonomous control, explainable AI, and regulatory compliance to enhance the safety and transparency of autonomous vehicles, supported by a case study and analysis.

Contribution

It introduces a novel design framework that combines autonomous driving, explainable AI, and regulation adherence, addressing safety and transparency challenges.

Findings

Framework effectively integrates control, XAI, and regulation.

Initial validation shows promising results.

Provides analysis of relevant XAI approaches.

Abstract

There has been recent and growing interest in the development and deployment of autonomous vehicles, encouraged by the empirical successes of powerful artificial intelligence techniques (AI), especially in the applications of deep learning and reinforcement learning. However, as demonstrated by recent traffic accidents, autonomous driving technology is not fully reliable for safe deployment. As AI is the main technology behind the intelligent navigation systems of self-driving vehicles, both the stakeholders and transportation regulators require their AI-driven software architecture to be safe, explainable, and regulatory compliant. In this paper, we propose a design framework that integrates autonomous control, explainable AI (XAI), and regulatory compliance to address this issue, and then provide an initial validation of the framework with a critical analysis in a case study. Moreover, we describe relevant XAI approaches that can help achieve the goals of the framework.