In a previous work, we have developed a template (or pattern) matching algorithm for maneuver recognition in highway traffic based on the dynamic time warping alignment strategy. The developed method rely on the model set-ups and tuning parameters, in particular, a template, pre-selected during the training phase. However, the selected template is representing to some extent an “in average” good solution, and the other ones performing less well in general, but maybe better for some particular cases, were rejected. In the current paper we are aiming to introduce a Linear Functional Strategy (LFS) to the community which allows to gain from the variety of possible solutions. This technique belongs to the so-called aggregation approaches, where different models are combined into a final estimator. In our case study, the resulting solution not only allows to avoid pre-selecting of a single template, but also outperforms the best single choice. The latter will be illustrated by numerical experiments on maneuver identification based on a big drone dataset of highway driving.