  The vision of automated driving is to increase both road safety and
efficiency, while offering passengers a convenient travel experience. This
requires that autonomous systems correctly estimate the current traffic scene
and its likely evolution. In highway scenarios early recognition of cut-in
maneuvers is essential for risk-aware maneuver planning. In this paper, a
statistical approach is proposed, which advantageously utilizes a set of
prototypical lane change trajectories to realize both early maneuver detection
and uncertainty-aware trajectory prediction for traffic participants.
Generation of prototype trajectories from real traffic data is accomplished by
Agglomerative Hierarchical Clustering. During clustering, the alignment of the
cluster prototypes to each other is optimized and the cohesion of the resulting
prototype is limited when two clusters merge. In the prediction stage, the
similarity of observed vehicle motion and typical lane change patterns in the
data base is evaluated to construct a set of significant features for maneuver
classification via Boosted Decision Trees. The future trajectory is predicted
combining typical lane change realizations in a mixture model. B-splines based
trajectory adaptations guarantee continuity during transition from actually
observed to predicted vehicle states. Quantitative evaluation results
demonstrate the proposed concept's improved performance for both maneuver and
trajectory prediction compared to a previously implemented reference approach.
