The potential of clustering methods to define intersection test scenarios: Assessing real-life performance of AEB.

Intersection accidents are frequent and harmful. The accident types 'straight crossing path' (SCP), 'left turn across path - oncoming direction' (LTAP/OD), and 'left-turn across path - lateral direction' (LTAP/LD) represent around 95% of all intersection accidents and one-third of all police-reported car-to-car accidents in Germany. The European New Car Assessment Program (Euro NCAP) have announced that intersection scenarios will be included in their rating from 2020; however, how these scenarios are to be tested has not been defined. This study investigates whether clustering methods can be used to identify a small number of test scenarios sufficiently representative of the accident dataset to evaluate Intersection Automated Emergency Braking (AEB). Data from the German In-Depth Accident Study (GIDAS) and the GIDAS-based Pre-Crash Matrix (PCM) from 1999 to 2016, containing 784 SCP and 453 LTAP/OD accidents, were analyzed with principal component methods to identify variables that account for the relevant total variances of the sample. Three different methods for data clustering were applied to each of the accident types, two similarity-based approaches, namely Hierarchical Clustering (HC) and Partitioning Around Medoids (PAM), and the probability-based Latent Class Clustering (LCC). The optimum number of clusters was derived for HC and PAM with the silhouette method. The PAM algorithm was both initiated with random start medoid selection and medoids from HC. For LCC, the Bayesian Information Criterion (BIC) was used to determine the optimal number of clusters. Test scenarios were defined from optimal cluster medoids weighted by their real-life representation in GIDAS. The set of variables for clustering was further varied to investigate the influence of variable type and character. We quantified how accurately each cluster variation represents real-life AEB performance using pre-crash simulations with PCM data and a generic algorithm for AEB intervention. The usage of different sets of clustering variables resulted in substantially different numbers of clusters. The stability of the resulting clusters increased with prioritization of categorical over continuous variables. For each different set of cluster variables, a strong in-cluster variance of avoided versus non-avoided accidents for the specified Intersection AEB was present. The medoids did not predict the most common Intersection AEB behavior in each cluster. Despite thorough analysis using various cluster methods and variable sets, it was impossible to reduce the diversity of intersection accidents into a set of test scenarios without compromising the ability to predict real-life performance of Intersection AEB. Although this does not imply that other methods cannot succeed, it was observed that small changes in the definition of a scenario resulted in a different avoidance outcome. Therefore, we suggest using limited physical testing to validate more extensive virtual simulations to evaluate vehicle safety.