Cluster Analysis of Vehicle Measurement Data for Improved Understanding of Behavior in Urban and Highway Environments

The comprehension of the behavior of objects in the environment of an automated vehicle is essential information to be extracted out of traffic measurement data. It can be made accessible by applying a data description process consisting of a cluster analysis and subsequent classification step. In this paper, two different algorithms, TRACLUS and Self-Organizing Maps are utilized for a clustering of laser scanner measurement data, recorded by experimental vehicles in real traffic. The results are evaluated and discussed. A remaining open issue of a cluster analysis is the necessity for a classification of clusters, which is missing or solved through manual solutions in related work. We address the matter with a novel approach for an automated classification of clusters using reference trajectories, which is successfully validated for a first exemplary maneuver on highway measurement data.