  Automated Driving Systems (ADS) development relies on utilizing real-world
vehicle data. The volume of data generated by modern vehicles presents
transmission, storage, and computational challenges. Focusing on Dynamic
Behavior (DB) offers a promising approach to distinguish relevant from
irrelevant information for ADS functionalities, thereby reducing data. Time
series pattern recognition is beneficial for this task as it can analyze the
temporal context of vehicle driving behavior. However, existing
state-of-the-art methods often lack the adaptability to identify
variable-length patterns or provide analytical descriptions of discovered
patterns. This contribution proposes a Behavior Forest framework for real-time
data selection by constructing a Behavior Graph during vehicle operation,
facilitating analytical descriptions without pre-training. The method
demonstrates its performance using a synthetically generated and
electrocardiogram data set. An automotive time series data set is used to
evaluate the data reduction capabilities, in which this method discarded 96.01%
of the incoming data stream, while relevant DB remain included.
