Risk Assessment Methodologies for Autonomous Driving: A Survey

Autonomous driving systems (ADS) in recent years have been the subject of focus, evolving as one of the major mobility disruptors and being a potential candidate for deployment in urban cities due to urbanization. ADS is the system within the Autonomous Vehicle (AV) that enables automation. The diff...

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Published inIEEE transactions on intelligent transportation systems Vol. 23; no. 10; pp. 16923 - 16939
Main Authors Chia, Wei Ming Dan, Keoh, Sye Loong, Goh, Cindy, Johnson, Christopher
Format Journal Article
LanguageEnglish
Published New York IEEE 01.10.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Artificial intelligence
Automation
Automotive engineering
Autonomous vehicle
Autonomous vehicles
Driving
intelligent vehicle
ISO Standards
Machine learning
risk analysis
Risk assessment
Safety
Urbanization
vehicle safety management
Vehicles
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Abstract Autonomous driving systems (ADS) in recent years have been the subject of focus, evolving as one of the major mobility disruptors and being a potential candidate for deployment in urban cities due to urbanization. ADS is the system within the Autonomous Vehicle (AV) that enables automation. The different ADS technologies that enable autonomous vehicles have reached a certain maturity that no longer focus on technological deployment, but rather on the safe deployment on public roads. However, existing standards that validate functional safety and Risk Assessment (RA) may not be sufficient to tackle the increased complexity of ADS compared to traditional vehicles. This demand in ADS safety is exponentially increasing in tandem with the increase of AV automation levels. ADS are exposed to diverse environmental conditions and therefore subjected to operational risks while attempting to mimic the human driver responses. Moreover, the recent use of artificial intelligence and machine learning in the industry further shapes the way how ADS development will become in the future. This paper explains the importance of RA coverage for AV and provides a comparison and summary of existing RA methodologies. Thereafter, a recommendation of RAs for AV as potential solutions in meeting ISO 26262 and ISO/PAS 21448 standards.
AbstractList Autonomous driving systems (ADS) in recent years have been the subject of focus, evolving as one of the major mobility disruptors and being a potential candidate for deployment in urban cities due to urbanization. ADS is the system within the Autonomous Vehicle (AV) that enables automation. The different ADS technologies that enable autonomous vehicles have reached a certain maturity that no longer focus on technological deployment, but rather on the safe deployment on public roads. However, existing standards that validate functional safety and Risk Assessment (RA) may not be sufficient to tackle the increased complexity of ADS compared to traditional vehicles. This demand in ADS safety is exponentially increasing in tandem with the increase of AV automation levels. ADS are exposed to diverse environmental conditions and therefore subjected to operational risks while attempting to mimic the human driver responses. Moreover, the recent use of artificial intelligence and machine learning in the industry further shapes the way how ADS development will become in the future. This paper explains the importance of RA coverage for AV and provides a comparison and summary of existing RA methodologies. Thereafter, a recommendation of RAs for AV as potential solutions in meeting ISO 26262 and ISO/PAS 21448 standards.
Author Johnson, Christopher
Goh, Cindy
Chia, Wei Ming Dan
Keoh, Sye Loong
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Snippet Autonomous driving systems (ADS) in recent years have been the subject of focus, evolving as one of the major mobility disruptors and being a potential...
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SubjectTerms Artificial intelligence
Automation
Automotive engineering
Autonomous vehicle
Autonomous vehicles
Driving
intelligent vehicle
ISO Standards
Machine learning
risk analysis
Risk assessment
Safety
Urbanization
vehicle safety management
Vehicles
Title Risk Assessment Methodologies for Autonomous Driving: A Survey
URI https://ieeexplore.ieee.org/document/9756641
https://www.proquest.com/docview/2723901238
Volume 23
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