A Maneuver-based Urban Driving Dataset and Model for Cooperative Vehicle Applications
This work addresses data needs for cooperative vehicle applications, but it is incremental as it builds on existing datasets and models.
The authors tackled the need for driving data in hybrid automated/human-driven traffic by introducing a real-world maneuver-based urban driving dataset and a model for classification and prediction.
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.