Automatic labeling and qualification of functional scenarios on the basis of sparse field observations

At the dawn of the deployment of connected and automated vehicles (CAVs) on our roads, assessing the safety of new systems is crucial. Given the overwhelming number of situations to test, focusing efforts on the most relevant ones for the system is essential. Qualifying scenarios with respect to their relevance is a challenging task. The scope of relevancy must be defined, and a labeling process applicable to any scenario must be developed. However, gathering information on various scenarios to label them poses a challenge because the flagrant lacks field data. In this study, we assume that relevancy is depicted by a safety criticality level on the basis of time-to-collision. We develop a labeling process for scenarios. It learns latent connections between the words generating scenarios and takes advantage of the latent structure to associate criticality levels with any scenario. Such a prediction model enables one to cope with the lack of data by ensuring the prior qualification of any scenario regardless of the quantity of field observations. This process is applied to scenarios described at a high level of abstraction, called functional scenarios. Criticality levels might be used to guide the application of the sampling strategy to select the scenarios under consideration when testing CAVs. Compared with field observations, the results of our automated process are highly correlated, with $R^2$ values of up to 0.835 on average.