Formulating a dissimilarity metric for comparison of driving scenarios for Automated Driving Systems

Safety assessment is one of the main challenges in deploying Automated Driving Systems (ADSs) on public roads. Scenario-based assessment is a common method to test such systems. Such scenario-based testing involves modeling the ADSs in a simulation environment to examine and evaluate their safety. Due to the complexity and uncertainty of the driving environment, the number of possible scenarios that ADSs can encounter is virtually infinite and there is a need for reduction of possible scenarios to a finite set. This research presents a generic framework to formulate a dissimilarity metric, which focuses on the comparison of driving scenarios on their most critical scenes, to reduce the number of possible scenarios into a finite and computationally manageable set.