This paper describes a method for the segmentation of dynamic data. It extends well known algorithms developed in the context of static clustering (e.g., the c-means algorithm, Kohonen maps, elastic nets and fuzzy c-means). The work is based on an unified framework for constrained clustering recently proposed by the authors in [1]. This framework is extended by using a motion model for the clus...

For intelligent driving systems, the ability to recognize different styles of surrounding vehicles is crucial in determining safest, yet more efficient decisions especially context mixed environment. Knowing for instance if vehicle adjacent lane aggressive or cautious can greatly assist decision making ego terms whether and when it appropriate make particular manoeuvres (e.g. change). In additi...

II Acknowledgements III List of Figures VI

Clustering is a powerful tool for data classification; however, its application has been limited to analysis of static snapshots of data which may be time-evolving. This work presents a clustering algorithm that employs a fixed time interval and a time-aggregated similarity measure to determine classification. The fixed time interval and a weighting parameter are tuned to the system’s dynamics;...