  Autonomous vehicles require reliable and resilient sensor suites and ongoing
validation through fleet-wide data collection. This paper proposes a Smart
Black Box (SBB) to augment traditional low-bandwidth data logging with
value-driven high-bandwidth data capture. The SBB caches short-term histories
of data as buffers through a deterministic Mealy machine based on data value
and similarity. Compression quality for each frame is determined by optimizing
the trade-off between value and storage cost. With finite storage, prioritized
data recording discards low-value buffers to make room for new data. This paper
formulates SBB compression decision making as a constrained multi-objective
optimization problem with novel value metrics and filtering. The SBB has been
evaluated on a traffic simulator which generates trajectories containing events
of interest (EOIs) and corresponding first-person view videos. SBB compression
efficiency is assessed by comparing storage requirements with different
compression quality levels and event capture ratios. Performance is evaluated
by comparing results with a traditional first-in-first-out (FIFO) recording
scheme. Deep learning performance on images recorded at different compression
levels is evaluated to illustrate the reproducibility of SBB recorded data.
