Risk Assessment Methodologies for Autonomous Driving: A Survey
Wei Ming Dan Chia, Sye Loong Keoh, Cindy Goh, Christopher Johnson
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.