The Safety Risks of AI-Driven Solutions in Autonomous Road Vehicles

Abstract

At present Deep Neural Networks (DNN) have a dominant role in the AI-driven Autonomous driving approaches. This paper focuses on the potential safety risks of deploying DNN classifiers in Advanced Driver Assistance System (ADAS) systems. In our experience, many theoretically sound AI-driven solutions tested and deployed in ADAS have shown serious safety flaws in practice. A brief review of practice and theory of automotive safety standards and related body of knowledge is presented. It is followed by a comparative analysis between DNN classifiers and safety standards developed in the automotive industry. The output of the study provides advice and recommendations for filling the current gaps within the complex and interrelated factors pertaining to the safety of Autonomous Road Vehicles (ARV). This study may assist ARV’s safety, system, and technology providers during the design, development, and implementation life cycle. The contribution of this work is to highlight and link the learning rules enforced by risk factors when DNN classifiers are expected to provide a near real-time safer Vehicle Navigation Solution (VNS).