Development of a Semi-Automatic Data Annotation Tool for Driving Data

Data-driven approaches to constructing context aware driver assistance systems require large annotated databases of automobile sensor data. Manually annotating such large databases is costly and time-consuming. We present a semi-automatic annotation tool for this purpose that uses random forests as bootstrapped classifiers. The tool significantly reduces the manual annotation effort by enabling the user to verify automatically generated annotations, rather than annotating from scratch