How to Rationalise the Sampling of Test-Scenarios in Automated Driving Based on Criticality Metrics?

The deployment of Automated and Connected Vehicles (ACV) into traffic requires certifications and validations guaranteeing high levels of safety, security and reliability. The underlying objective is to gain public acceptance by proving that automation systems might bring out a safer mobility. While plenty of methods to certify these systems are populating the literature, the scenario-based approach stands out by reducing the quantity of required Field tests to validate any new system at stake. In this study, we refine the scenario-based approach by proposing a proof of concept (PoC) for scenario reduction using criticality metrics. For this PoC, we weave a relationship between the a priori criticality of abstract functional scenarios and the words used to generate them. Once, the criticality of a subset of scenarios is qualified based on open field data (HighD), the Latent Dirichlet Allocation (LDA) clustering approach is used to generate topics and feature the relationship between observed criticality and semantics words applied to functional scenarios. The criticality degree of semantics words is used to predict the a priori criticality of unobserved functional scenarios.