An Approach to Guide the Search for Potentially Hazardous Scenarios for Autonomous Vehicle Safety Validation

Abstract

Safety validation of Autonomous Vehicles (AV) requires simulation. Automotive manufacturers need to generate scenarios used during this simulation-based validation process. Several approaches have been proposed to master scenario generation. However, none have proposed a method to measure the potential hazardousness of the scenarios with regard to the performance limitations of AV. In other words, there is no method offering a metric to guide the search for potentially critical scenarios within the infinite space of scenarios. However, designers have knowledge of the functional limitations of AV components depending on the situations encountered. The more sensitive the AV is to a situation, the more safety experts consider it to be critical. In this paper, we present a new method to help estimate the sensitivity of AV to logical situations and events before their use for the generation of concrete scenarios submitted to simulators. We propose a characterization of the inputs used for sensitivity analysis (definition of the context of the automation function, generation of functional and logical situations with their associated events). We then propose an approach to set up a distribution function that will make it possible to select situations and events according to their importance in terms of sensitivity. We illustrate this approach by implementing it on the Traffic Jam Chauffeur (TJC) function. Finally, we compare the obtained sensitivity rank with expert judgment to demonstrate its relevance. This approach has been shown to be a promising method to guide the search for potentially hazardous scenarios that are relevant to the simulation-based safety validation process for AV.