Real-time Recursive Risk Assessment Framework for Autonomous Vehicle Operations

Chia, W. M. D., Keoh, S. L. , Michala, A. L. and Goh, C. (2021) Real-time Recursive Risk Assessment Framework for Autonomous Vehicle Operations. In: 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), 25-28 Apr 2021, ISBN 9781728189642

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Abstract

Existing risk assessment (RA) methodology used for autonomous vehicle (AV) development and validation is insufficient for future AV operations. Existing frameworks operate based on processes such as hazard analysis and risk assessment (HARA) where risk is defined based on functional hazardous event severity and the likelihood of occurrence. This is a static process performed during the development stage and relies on prior lessons learnt and know-how. A drawback of this is the omission of potential complex environments that could occur during real-time – especially with more stringent safety requirements for AV operating at higher automation levels. Therefore, there is a need for an additional framework to further enhance the safety levels of the AV, focusing on real-time instead of static risk assessment during development. In this paper, a novel real-time recursive RA framework (ReRAF) addresses the gap by creating a novel risk representation, predictive risk number (PRN), and eventual safety levels (SLs) in the temporal and spatial domain. This approach focuses on risk assessment based on AV collision to the detected hazardous object and controllability of the AV. A dynamic recursive RA continuously captures potentially hazardous events in real-time and compares them with past occurrences to predict future safety actions. ReRAF provides a continuous improvement on the RA and acts as an additional safety layer for AV operations.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Goh, Dr Cindy Sf and Keoh, Dr Sye Loong and Chia, Wei Ming and Michala, Dr Lito
Authors: Chia, W. M. D., Keoh, S. L., Michala, A. L., and Goh, C.
College/School:College of Science and Engineering > School of Computing Science
College of Science and Engineering > School of Engineering
Research Group:GLASS
ISSN:2577-2465
ISBN:9781728189642
Published Online:15 June 2021
Copyright Holders:Copyright © 2021 IEEE
First Published:First published in 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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