Run-Time Safety Monitoring Framework for AI-Based Systems: Automated Driving Cases

Intelligent systems based on artificial intelligence techniques are increasing and are recently being accepted in the automotive domain. In the competition of automobile makers to provide fully automated vehicles, it is perceived that artificial intelligence will profoundly influence the automotive electric and electronic architecture in the future. However, while such systems provide highly advanced functions, safety risk increases as AI-based systems may produce uncertain output and behaviour. In this paper, we devise a run-time safety monitoring framework for AI-based intelligence systems focusing on autonomous driving functions. In detail, this paper describes (i) the characteristics of a safety monitoring framework; (ii) the safety monitoring framework itself, and (iii) we develop a prototype and implement the framework for two critical driving functions: Lane detection and object detection. Through an implementation of the framework to a prototypic control environment, we show the possibility of this framework in the real context. Finally, we discuss the techniques used in developing the safety monitoring framework and describes the encountered challenges.

I. Introduction

Context: During the last decades, a lot of software-controlled functions running on a large number of electronic control units were included in modern car electric/electronic (E/E) architectures. Driving forces are (i) safety requirements, (ii) customer demand for more comfort and the newest infotainment systems, and (iii) advanced driver assistance systems allowing to reach hitherto unrivalled levels of driving automation (cf. [1]).