The University of Southampton
University of Southampton Institutional Repository

A rigorous analysis template process to capture the safety properties of self-driving vehicle systems

A rigorous analysis template process to capture the safety properties of self-driving vehicle systems
A rigorous analysis template process to capture the safety properties of self-driving vehicle systems
Self-Driving Vehicles (SDVs) are seen as a significant advancement in the automotive domain, hinting at a future where human drivers might be rendered obsolete. However, even with the advancements in SDV technology, the need for human drivers is still recognised. The incorporation of human drivers into SDVs introduces unique and significant challenges. The significance of human driver and SDV interactions cannot be overstated, especially when the SDV relies on the human driver as a fallback option during hazardous driving events. To address this critical aspect, this thesis presents a methodology termed the Rigorous Analysis Template Process (RATP). RATP establishes an analytical journey to develop a comprehensive framework ensuring safety and optimal cooperation between human drivers and SDV systems. It represents an evolution in existing work on analysing system safety and provides a more rigorous systematic strategy for SDV systems. It involves both systematic analysis and formal methods to evaluate safety in SDV systems. Drawing strength from a combination of both systematic analysis and formal methods, RATP adeptly identifies high-level safety requirements and develops a rigorous model to investigate issues and assumptions that may arise during the operations of SDV systems. One of the key benefits of RATP is its modularity, offering researchers and developers the ability to systematically analyse system behaviours from a high-abstraction view down to a more detailed view. The conclusion of this research presents a robust set of modelling patterns that act as a blueprint for the future development of SDV systems. RATP is demonstrated with a case study that explores the various functionalities of an SDV system to evolve the methodology into a mature state. Finally, this thesis presents a discussion on future improvements that could be undertaken to develop the methodology further.
University of Southampton
Alotaibi, Fahad Abduallah
3606f09d-b071-47de-b0c2-f9784f7b1abd
Alotaibi, Fahad Abduallah
3606f09d-b071-47de-b0c2-f9784f7b1abd
Hoang, Son
dcc0431d-2847-4e1d-9a85-54e4d6bab43f
Butler, Michael
54b9c2c7-2574-438e-9a36-6842a3d53ed0

Alotaibi, Fahad Abduallah (2024) A rigorous analysis template process to capture the safety properties of self-driving vehicle systems. University of Southampton, Doctoral Thesis, 315pp.

Record type: Thesis (Doctoral)

Abstract

Self-Driving Vehicles (SDVs) are seen as a significant advancement in the automotive domain, hinting at a future where human drivers might be rendered obsolete. However, even with the advancements in SDV technology, the need for human drivers is still recognised. The incorporation of human drivers into SDVs introduces unique and significant challenges. The significance of human driver and SDV interactions cannot be overstated, especially when the SDV relies on the human driver as a fallback option during hazardous driving events. To address this critical aspect, this thesis presents a methodology termed the Rigorous Analysis Template Process (RATP). RATP establishes an analytical journey to develop a comprehensive framework ensuring safety and optimal cooperation between human drivers and SDV systems. It represents an evolution in existing work on analysing system safety and provides a more rigorous systematic strategy for SDV systems. It involves both systematic analysis and formal methods to evaluate safety in SDV systems. Drawing strength from a combination of both systematic analysis and formal methods, RATP adeptly identifies high-level safety requirements and develops a rigorous model to investigate issues and assumptions that may arise during the operations of SDV systems. One of the key benefits of RATP is its modularity, offering researchers and developers the ability to systematically analyse system behaviours from a high-abstraction view down to a more detailed view. The conclusion of this research presents a robust set of modelling patterns that act as a blueprint for the future development of SDV systems. RATP is demonstrated with a case study that explores the various functionalities of an SDV system to evolve the methodology into a mature state. Finally, this thesis presents a discussion on future improvements that could be undertaken to develop the methodology further.

Text
Fahad_Alotaibi_Doctoral_thesis_PDFA - Version of Record
Available under License University of Southampton Thesis Licence.
Download (9MB)
Text
Final-thesis-submission-Examination-Mr-Fahad-Alotaibi
Restricted to Repository staff only

More information

Published date: March 2024

Identifiers

Local EPrints ID: 488315
URI: http://eprints.soton.ac.uk/id/eprint/488315
PURE UUID: db481b0a-06c0-421d-8fd2-c31acd6e0619
ORCID for Fahad Abduallah Alotaibi: ORCID iD orcid.org/0000-0001-8545-907X
ORCID for Son Hoang: ORCID iD orcid.org/0000-0003-4095-0732
ORCID for Michael Butler: ORCID iD orcid.org/0000-0003-4642-5373

Catalogue record

Date deposited: 19 Mar 2024 18:41
Last modified: 20 Mar 2024 02:58

Export record

Contributors

Author: Fahad Abduallah Alotaibi ORCID iD
Thesis advisor: Son Hoang ORCID iD
Thesis advisor: Michael Butler ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×