  Autonomous vehicles require fleet-wide data collection for continuous
algorithm development and validation. The Smart Black Box (SBB) intelligent
event data recorder has been proposed as a system for prioritized
high-bandwidth data capture. This paper extends the SBB by applying anomaly
detection and action detection methods for generalized event-of-interest (EOI)
detection. An updated SBB pipeline is proposed for the real-time capture of
driving video data. A video dataset is constructed to evaluate the SBB on
real-world data for the first time. SBB performance is assessed by comparing
the compression of normal and anomalous data and by comparing our prioritized
data recording with a FIFO strategy. Results show that SBB data compression can
increase the anomalous-to-normal memory ratio by ~25%, while the prioritized
recording strategy increases the anomalous-to-normal count ratio when compared
to a FIFO strategy. We compare the real-world dataset SBB results to a baseline
SBB given ground-truth anomaly labels and conclude that improved general EOI
detection methods will greatly improve SBB performance.
