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A Method for the Runtime Monitoring of AI-based Environment Perception in Automated Driving Systems

Authors:
Iqra Aslam
Abhishek Buragohain
Daniel Bamal
Adina Aniculaesei
Meng Zhang
Andreas Rausch

Keywords: runtime monitoring; function monitor; dependable safety-critical system; automated driving system; perception system

Abstract:
Environment perception is a fundamental part of the dynamic driving task executed by Autonomous Driving Systems (ADS). Artificial Intelligence (AI)-based approaches have prevailed over classical techniques for realizing the environment perception. Current safety-relevant standards for automotive systems, ISO 26262 and ISO 21448, assume the existence of comprehensive requirements specifications. These specifications serve as the basis on which the functionality of an automotive system can be rigorously tested and checked for compliance with safety regulations. However, AI-based perception systems do not have complete requirements specification. Instead, large datasets are used to train AI-based perception systems. This paper presents a function monitor for the functional runtime monitoring of a two-folded AI-based environment perception for ADS, based respectively on camera and LiDAR sensors. To evaluate the applicability of the function monitor, we conduct a qualitative scenario-based evaluation in a controlled laboratory environment using a model car. The evaluation results then are discussed to provide insights into the monitor's performance and its suitability for real-world applications.

Pages: 17 to 25

Copyright: Copyright (c) IARIA, 2024

Publication date: April 14, 2024

Published in: conference

ISSN: 2308-4146

ISBN: 978-1-68558-153-4

Location: Venice, Italy

Dates: from April 14, 2024 to April 18, 2024