  Short-term future of automated driving can be imagined as a hybrid scenario
in which both automated and human-driven vehicles co-exist in the same
environment. In order to address the needs of such road configuration, many
technology solutions such as vehicular communication and predictive control for
automated vehicles have been introduced in the literature. Both aforementioned
solutions rely on driving data of the human driver. In this work, we
investigate the currently available driving datasets and introduce a real-world
maneuver-based driving dataset that is collected during our urban driving data
collection campaign. We also provide a model that embeds the patterns in
maneuver-specific samples. Such model can be employed for classification and
prediction purposes.
