Towards Safe, Explainable, and Regulated Autonomous Driving

There has been growing interest in the development and deployment of autonomous vehicles on roads over the last few years, encouraged by the empirical successes of powerful artificial intelligence techniques (AI), especially in the applications of deep and reinforcement learning. However, recent traffic accidents with autonomous cars prevent this technology from being publicly acceptable at a broader level. As AI is the main driving force behind the intelligent navigation systems of self-driving vehicles, both the stakeholders and transportation jurisdictions require their AI-driven software architecture to be safe, explainable, and regulatory compliant. We present a framework that integrates autonomous control, explainable AI architecture, and regulatory compliance to address this issue. Moreover, we provide several conceptual models from this perspective to help guide future research directions.