Towards Safe, Explainable, and Regulated Autonomous Driving
This addresses safety and regulatory concerns for stakeholders and transportation regulators in autonomous vehicles, but appears incremental as it builds on existing XAI approaches.
The paper tackles the problem of unreliable autonomous driving technology by proposing a design framework that integrates autonomous control, explainable AI, and regulatory compliance, with initial validation through a case study.
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.